Search Word(s) in Title, Keywords, Authors, and Abstract: Features

An Efficient Fingerprint Matching by Multiple Reference Points
Kittiya Khongkraphan
Page: 22~33, Vol. 15, No.1, 2019
10.3745/JIPS.04.0098
Keywords: Fingerprint Matching, Matching Score, Multiple Reference Points, Non-linear Distortion
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This paper introduces an efficient fingerprint matching method based on multiple reference minutiae points. First, we attempt to effectively align two fingerprints by employing multiple reference minutiae points. However, the corresponding minutiae points between two fingerprints are ambiguous since a minutia of one fingerprint can be a match to any minutia of the other fingerprint. Therefore, we introduce a novel method based on linear classification concept to establish minutiae correspondences between two fingerprints. Each minutiae correspondence represents a possible alignment. For each possible alignment, a matching score is computed using minutiae and ridge orientation features and the maximum score is then selected to represent the similarity of the two fingerprints. The proposed method is evaluated using fingerprint databases, FVC2002 and FVC2004. In addition, we compare our approach with two existing methods and find that our approach outperforms them in term of matching accuracy, especially in the case of non-linear distorted fingerprints. Furthermore, the experiments show that our method provides additional advantages in low quality fingerprint images such as inaccurate position, missing minutiae, and spurious extracted minutiae.


Feature Extraction based on DBN-SVM for Tone Recognition
Hao Chao, Cheng Song, Bao-yun Lu and Yong-li Liu
Page: 91~99, Vol. 15, No.1, 2019
10.3745/JIPS.04.0101
Keywords: Deep Belief Networks, Deep Learning, Feature Fusion, Support Vector Machine, Tone Feature Extraction
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An innovative tone modeling framework based on deep neural networks in tone recognition was proposed in this paper. In the framework, both the prosodic features and the articulatory features were firstly extracted as the raw input data. Then, a 5-layer-deep deep belief network was presented to obtain high-level tone features. Finally, support vector machine was trained to recognize tones. The 863-data corpus had been applied in experiments, and the results show that the proposed method helped improve the recognition accuracy significantly for all tone patterns. Meanwhile, the average tone recognition rate reached 83.03%, which is 8.61% higher than that of the original method.


Forest fire detection and identification using image processing and SVM
Mubarak Adam Ishag Mahmoud and Honge Ren
Page: 159~168, Vol. 15, No.1, 2019
10.3745/JIPS.01.0038
Keywords: Background Subtraction, CIE L?a?b? Color Space, Forest Fire, SVM, Wavelet
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Accurate forest fires detection algorithms remain a challenging issue, because, some of the objects have the same features with fire, which may result in high false alarms rate. This paper presents a new video-based, image processing forest fires detection method, which consists of four stages. First, a background-subtraction algorithm is applied to detect moving regions. Secondly, candidate fire regions are determined using CIE L?a?b? color space. Thirdly, special wavelet analysis is used to differentiate between actual fire and fire-like objects, because candidate regions may contain moving fire-like objects. Finally, support vector machine is used to classify the region of interest to either real fire or non-fire. The final experimental results verify that the proposed method effectively identifies the forest fires.


Future Trends of AI-based Smart Systems and Services: Challenges, Opportunities, and Solutions
Daewon Lee and Jong Hyuk Park
Page: 717~723, Vol. 15, No.4, 2019
10.3745/JIPS.02.0113
Keywords: Artificial Intelligence, Deep Learning, Sentiment Analysis, Smart Systems and Services
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Smart systems and services aim to facilitate growing urban populations and their prospects of virtual-real social behaviors, gig economies, factory automation, knowledge-based workforce, integrated societies, modern living, among many more. To satisfy these objectives, smart systems and services must comprises of a complex set of features such as security, ease of use and user friendliness, manageability, scalability, adaptivity, intelligent behavior, and personalization. Recently, artificial intelligence (AI) is realized as a data-driven technology to provide an efficient knowledge representation, semantic modeling, and can support a cognitive behavior aspect of the system. In this paper, an integration of AI with the smart systems and services is presented to mitigate the existing challenges. Several novel researches work in terms of frameworks, architectures, paradigms, and algorithms are discussed to provide possible solutions against the existing challenges in the AI-based smart systems and services. Such novel research works involve efficient shape image retrieval, speech signal processing, dynamic thermal rating, advanced persistent threat tactics, user authentication, and so on.


Shape Description and Retrieval Using Included-Angular Ternary Pattern
Guoqing Xu, Ke Xiao and Chen Li
Page: 737~747, Vol. 15, No.4, 2019
10.3745/JIPS.02.0114
Keywords: Image Retrieval, Included-Angular Ternary Pattern, Multiscale, Shape Description
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Shape description is an important and fundamental issue in content-based image retrieval (CBIR), and a number of shape description methods have been reported in the literature. For shape description, both global information and local contour variations play important roles. In this paper a new included-angular ternary pattern (IATP) based shape descriptor is proposed for shape image retrieval. For each point on the shape contour, IATP is derived from its neighbor points, and IATP has good properties for shape description. IATP is intrinsically invariant to rotation, translation and scaling. To enhance the description capability, multiscale IATP histogram is presented to describe both local and global information of shape. Then multiscale IATP histogram is combined with included-angular histogram for efficient shape retrieval. In the matching stage, cosine distance is used to measure shape features’ similarity. Image retrieval experiments are conducted on the standard MPEG-7 shape database and Swedish leaf database. And the shape image retrieval performance of the proposed method is compared with other shape descriptors using the standard evaluation method. The experimental results of shape retrieval indicate that the proposed method reaches higher precision at the same recall value compared with other description method.


CCTV-Based Multi-Factor Authentication System
Byoung-Wook Kwon, Pradip Kumar Sharma and Jong-Hyuk Park
Page: 904~919, Vol. 15, No.4, 2019
10.3745/JIPS.03.0127
Keywords: Argon2, Convolutional Neural Network, Deep Reinforcement Learning, Physically Unclonable Functions
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Many security systems rely solely on solutions based on Artificial Intelligence, which are weak in nature. These security solutions can be easily manipulated by malicious users who can gain unlawful access. Some security systems suggest using fingerprint-based solutions, but they can be easily deceived by copying fingerprints with clay. Image-based security is undoubtedly easy to manipulate, but it is also a solution that does not require any special training on the part of the user. In this paper, we propose a multi-factor security framework that operates in a three-step process to authenticate the user. The motivation of the research lies in utilizing commonly available and inexpensive devices such as onsite CCTV cameras and smartphone camera and providing fully secure user authentication. We have used technologies such as Argon2 for hashing image features and physically unclonable identification for secure device-server communication. We also discuss the methodological workflow of the proposed multi-factor authentication framework. In addition, we present the service scenario of the proposed model. Finally, we analyze qualitatively the proposed model and compare it with state-of-the-art methods to evaluate the usability of the model in real-world applications.


Kernel Fisher Discriminant Analysis for Natural Gait Cycle Based Gait Recognition
Jun Huang, Xiuhui Wang and Jun Wang
Page: 957~966, Vol. 15, No.4, 2019
10.3745/JIPS.02.0115
Keywords: Gait Energy Image, Gait Recognition, Kernel Fisher Discriminant Analysis, Natural Gait Cycle
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This paper studies a novel approach to natural gait cycles based gait recognition via kernel Fisher discriminant analysis (KFDA), which can effectively calculate the features from gait sequences and accelerate the recognition process. The proposed approach firstly extracts the gait silhouettes through moving object detection and segmentation from each gait videos. Secondly, gait energy images (GEIs) are calculated for each gait videos, and used as gait features. Thirdly, KFDA method is used to refine the extracted gait features, and low-dimensional feature vectors for each gait videos can be got. The last is the nearest neighbor classifier is applied to classify. The proposed method is evaluated on the CASIA and USF gait databases, and the results show that our proposed algorithm can get better recognition effect than other existing algorithms.


A Development of LDA Topic Association Systems Based on Spark-Hadoop Framework
Kiejin Park and Limei Peng
Page: 140~149, Vol. 14, No.1, 2018
10.3745/JIPS.04.0057
Keywords: Association Analysis, Hadoop, LDA (Latent Dirichlet Allocation), Spark, Topic Model
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Social data such as users’ comments are unstructured in nature and up-to-date technologies for analyzing such data are constrained by the available storage space and processing time when fast storing and processing is required. On the other hand, it is even difficult in using a huge amount of dynamically generated social data to analyze the user features in a high speed. To solve this problem, we design and implement a topic association analysis system based on the latent Dirichlet allocation (LDA) model. The LDA does not require the training process and thus can analyze the social users’ hourly interests on different topics in an easy way. The proposed system is constructed based on the Spark framework that is located on top of Hadoop cluster. It is advantageous of high-speed processing owing to that minimized access to hard disk is required and all the intermediately generated data are processed in the main memory. In the performance evaluation, it requires about 5 hours to analyze the topics for about 1 TB test social data (SNS comments). Moreover, through analyzing the association among topics, we can track the hourly change of social users’ interests on different topics.


DeepAct: A Deep Neural Network Model for Activity Detection in Untrimmed Videos
Yeongtaek Song and Incheol Kim
Page: 150~161, Vol. 14, No.1, 2018
10.3745/JIPS.04.0059
Keywords: Activity Detection, Bi-directional LSTM, Deep Neural Networks, Untrimmed Video
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We propose a novel deep neural network model for detecting human activities in untrimmed videos. The process of human activity detection in a video involves two steps: a step to extract features that are effective in recognizing human activities in a long untrimmed video, followed by a step to detect human activities from those extracted features. To extract the rich features from video segments that could express unique patterns for each activity, we employ two different convolutional neural network models, C3D and I-ResNet. For detecting human activities from the sequence of extracted feature vectors, we use BLSTM, a bi-directional recurrent neural network model. By conducting experiments with ActivityNet 200, a large-scale benchmark dataset, we show the high performance of the proposed DeepAct model.


A Deep Belief Network for Electricity Utilisation Feature Analysis of Air Conditioners Using a Smart IoT Platform
Wei Song, Ning Feng, Yifei Tian, Simon Fong and Kyungeun Cho
Page: 162~175, Vol. 14, No.1, 2018
10.3745/JIPS.04.0056
Keywords: Cloud Computing, Deep Belief Network, IoT, Power Conservation, Smart Metre
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Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user’s comfort and improving the user’s experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively


Convolutional Neural Network Based Multi-feature Fusion for Non-rigid 3D Model Retrieval
Hui Zeng, Yanrong Liu, Siqi Li, JianYong Che and Xiuqing Wang
Page: 176~190, Vol. 14, No.1, 2018
10.3745/JIPS.04.0058
Keywords: Convolutional Neural Network, HKS, Multi-Feature Fusion, Non-rigid 3D Model, WKS
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This paper presents a novel convolutional neural network based multi-feature fusion learning method for nonrigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method


Face Recognition Based on the Combination of Enhanced Local Texture Feature and DBN under Complex Illumination Conditions
Chen Li, Shuai Zhao, Ke Xiao and Yanjie Wang
Page: 191~204, Vol. 14, No.1, 2018
10.3745/JIPS.04.0060
Keywords: Deep Belief Network, Enhanced Local Texture Feature, Face Recognition, Illumination Variation
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To combat the adverse impact imposed by illumination variation in the face recognition process, an effective and feasible algorithm is proposed in this paper. Firstly, an enhanced local texture feature is presented by applying the central symmetric encode principle on the fused component images acquired from the wavelet decomposition. Then the proposed local texture features are combined with Deep Belief Network (DBN) to gain robust deep features of face images under severe illumination conditions. Abundant experiments with different test schemes are conducted on both CMU-PIE and Extended Yale-B databases which contain face images under various illumination condition. Compared with the DBN, LBP combined with DBN and CSLBP combined with DBN, our proposed method achieves the most satisfying recognition rate regardless of the database used, the test scheme adopted or the illumination condition encountered, especially for the face recognition under severe illumination variation.


Vocal Effort Detection Based on Spectral Information Entropy Feature and Model Fusion
Hao Chao, Bao-Yun Lu, Yong-Li Liu and Hui-Lai Zhi
Page: 218~227, Vol. 14, No.1, 2018
10.3745/JIPS.04.0063
Keywords: Gaussian Mixture Model, Model Fusion, Multilayer Perceptron, Spectral Information Entropy, Support Vector Machine, Vocal Effort
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Vocal effort detection is important for both robust speech recognition and speaker recognition. In this paper, the spectral information entropy feature which contains more salient information regarding the vocal effort level is firstly proposed. Then, the model fusion method based on complementary model is presented to recognize vocal effort level. Experiments are conducted on isolated words test set, and the results show the spectral information entropy has the best performance among the three kinds of features. Meanwhile, the recognition accuracy of all vocal effort levels reaches 81.6%. Thus, potential of the proposed method is demonstrated


Implementation of Multipurpose PCI Express Adapter Cards with On-Board Optical Module
Kyungmo Koo, Junglok Yu, Sangwan Kim, Min Choi and Kwangho Cha
Page: 270~279, Vol. 14, No.1, 2018
10.3745/JIPS.01.0022
Keywords: Device Network, Interconnection Network, On-Board Optical Module, PCI Express Bus
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PCI Express (PCIe) bus, which was only used as an internal I/O bus of a computer system, has expanded its function to outside of a system, with progress of PCIe switching processor. In particular, advanced features of PCIe switching processor enable PCIe bus to serve as an interconnection network as well as connecting external devices. As PCIe switching processors more advanced, it is required to consider the different adapter card architecture. This study developed multipurpose adapter cards by applying an on-board optical module, a latest optical communications element, in order to improve transfer distance and utilization. The performance evaluation confirmed that the new adapter cards with long cable can provide the same bandwidth as that of the existing adapter cards with short copper cable.


Security and Privacy in Ubiquitous Sensor Networks
Alfredo J. Perez, Sherali Zeadally and Nafaa Jabeur
Page: 286~308, Vol. 14, No.2, 2018
10.3745/JIPS.03.0094
Keywords: Human-Centric Sensing, Internet of Things, Opportunistic Sensing, Participatory Sensing, Privacy, Security, Ubiquitous Sensing
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The availability of powerful and sensor-enabled mobile and Internet-connected devices have enabled the advent of the ubiquitous sensor network (USN) paradigm. USN provides various types of solutions to the general public in multiple sectors, including environmental monitoring, entertainment, transportation, security, and healthcare. Here, we explore and compare the features of wireless sensor networks and USN. Based on our extensive study, we classify the security- and privacy-related challenges of USNs. We identify and discuss solutions available to address these challenges. Finally, we briefly discuss open challenges for designing more secure and privacy-preserving approaches in next-generation USNs.


GLIBP: Gradual Locality Integration of Binary Patterns for Scene Images Retrieval
Salah Bougueroua and Bachir Boucheham
Page: 469~486, Vol. 14, No.2, 2018
10.3745/JIPS.02.0081
Keywords: CBIR, Elliptic-Region, Global Information, LBP, Local Information, Texture
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We propose an enhanced version of the local binary pattern (LBP) operator for texture extraction in images in the context of image retrieval. The novelty of our proposal is based on the observation that the LBP exploits only the lowest kind of local information through the global histogram. However, such global Histograms reflect only the statistical distribution of the various LBP codes in the image. The block based LBP, which uses local histograms of the LBP, was one of few tentative to catch higher level textural information. We believe that important local and useful information in between the two levels is just ignored by the two schemas. The newly developed method: gradual locality integration of binary patterns (GLIBP) is a novel attempt to catch as much local information as possible, in a gradual fashion. Indeed, GLIBP aggregates the texture features present in grayscale images extracted by LBP through a complex structure. The used framework is comprised of a multitude of ellipse-shaped regions that are arranged in circular-concentric forms of increasing size. The framework of ellipses is in fact derived from a simple parameterized generator. In addition, the elliptic forms allow targeting texture directionality, which is a very useful property in texture characterization. In addition, the general framework of ellipses allows for taking into account the spatial information (specifically rotation). The effectiveness of GLIBP was investigated on the Corel-1K (Wang) dataset. It was also compared to published works including the very effective DLEP. Results show significant higher or comparable performance of GLIBP with regard to the other methods, which qualifies it as a good tool for scene images retrieval.


Feature Subset for Improving Accuracy of Keystroke Dynamics on Mobile Environment
Sung-Hoon Lee, Jong-hyuk Roh, SooHyung Kim and Seung-Hun Jin
Page: 523~538, Vol. 14, No.2, 2018
10.3745/JIPS.03.0093
Keywords: Feature Subset, Keystroke Dynamics, Smartphone Sensor
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Keystroke dynamics user authentication is a behavior-based authentication method which analyzes patterns in how a user enters passwords and PINs to authenticate the user. Even if a password or PIN is revealed to another user, it analyzes the input pattern to authenticate the user; hence, it can compensate for the drawbacks of knowledge-based (what you know) authentication. However, users' input patterns are not always fixed, and each user's touch method is different. Therefore, there are limitations to extracting the same features for all users to create a user's pattern and perform authentication. In this study, we perform experiments to examine the changes in user authentication performance when using feature vectors customized for each user versus using all features. User customized features show a mean improvement of over 6% in error equal rate, as compared to when all features are used.


Maximizing Network Utilization in IEEE 802.21 Assisted Vertical Handover over Wireless Heterogeneous Networks
Dinesh Pandey, Beom Hun Kim, Hui-Seon Gang, Goo-Rak Kwon and Jae-Young Pyun
Page: 771~789, Vol. 14, No.3, 2018
10.3745/JIPS.03.0099
Keywords: Handover Decision, IEEE 802.21, Occupied Bandwidth, SINR, Vertical Handover
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In heterogeneous wireless networks supporting multi-access services, selecting the best network from among the possible heterogeneous connections and providing seamless service during handover for a higher Quality of Services (QoSs) is a big challenge. Thus, we need an intelligent vertical handover (VHO) decision using suitable network parameters. In the conventional VHOs, various network parameters (i.e., signal strength, bandwidth, dropping probability, monetary cost of service, and power consumption) have been used to measure network status and select the preferred network. Because of various parameter features defined in each wireless/mobile network, the parameter conversion between different networks is required for a handover decision. Therefore, the handover process is highly complex and the selection of parameters is always an issue. In this paper, we present how to maximize network utilization as more than one target network exists during VHO. Also, we show how network parameters can be imbedded into IEEE 802.21- based signaling procedures to provide seamless connectivity during a handover. The network simulation showed that QoS-effective target network selection could be achieved by choosing the suitable parameters from Layers 1 and 2 in each candidate network.


Measuring the Degree of Content Immersion in a Non-experimental Environment Using a Portable EEG Device
Nam-Ho Keum, Taek Lee, Jung-Been Lee and Hoh Peter In
Page: 1049~1061, Vol. 14, No.4, 2018
10.3745/JIPS.04.0084
Keywords: Automated Collection, BCI, Measurement of Immersion, Noise Filtering, Non-experimental Environment, Portable EEG
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As mobile devices such as smartphones and tablet PCs become more popular, users are becoming accustomed to consuming a massive amount of multimedia content every day without time or space limitations. From the industry, the need for user satisfaction investigation has consequently emerged. Conventional methods to investigate user satisfaction usually employ user feedback surveys or interviews, which are considered manual, subjective, and inefficient. Therefore, the authors focus on a more objective method of investigating users’ brainwaves to measure how much they enjoy their content. Particularly for multimedia content, it is natural that users will be immersed in the played content if they are satisfied with it. In this paper, the authors propose a method of using a portable and dry electroencephalogram (EEG) sensor device to overcome the limitations of the existing conventional methods and to further advance existing EEG-based studies. The proposed method uses a portable EEG sensor device that has a small, dry (i.e., not wet or adhesive), and simple sensor using a single channel, because the authors assume mobile device environments where users consider the features of portability and usability to be important. This paper presents how to measure attention, gauge and compute a score of user’s content immersion level after addressing some technical details related to adopting the portable EEG sensor device. Lastly, via an experiment, the authors verified a meaningful correlation between the computed scores and the actual user satisfaction scores.


A Multi-Level Integrator with Programming Based Boosting for Person Authentication Using Different Biometrics
Sumana Kundu and Goutam Sarker
Page: 1114~1135, Vol. 14, No.5, 2018
10.3745/JIPS.02.0094
Keywords: Accuracy, Back Propagation Learning, Biometrics, HBC, F-score, Malsburg Learning, Mega-Super-Classifier, MOCA, Multiple Classification System, OCA, Person Identification, Precision, Recall, RBFN, SOM, Super- Classifier
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A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual superclassifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.


A Clustering Approach for Feature Selection in Microarray Data Classification Using Random Forest
Husna Aydadenta and Adiwijaya
Page: 1167~1175, Vol. 14, No.5, 2018
10.3745/JIPS.04.0087
Keywords: Classification, Clustering, Dimensional Reduction, Microarray, Random Forest
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Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.


A Hybrid Proposed Framework for Object Detection and Classification
Muhammad Aamir, Yi-Fei Pu, Ziaur Rahman, Waheed Ahmed Abro, Hamad Naeem, Farhan Ullah and Aymen Mudheher Badr
Page: 1176~1194, Vol. 14, No.5, 2018
10.3745/JIPS.02.0095
Keywords: Image Proposals, Feature Extraction, Object Classification, Object Detection, Segmentation
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The object classification using the images’ contents is a big challenge in computer vision. The superpixels’ information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it according to the region score. Further, this information is used to extract local and global features using a hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the classification accuracy, the feature fusion technique is applied to combine local and global features vectors through weight parameter. The support vector machine classifier is a supervised algorithm is used for classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007 (VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in high-quality class for independent objects’ locations with a mean average best overlap (MABO) of 0.833 at 1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it is proved that it gave the better classification results for the non-rigid classes.


Semantic Image Search: Case Study for Western Region Tourism in Thailand
Chantana Chantrapornchai, Netnapa Bunlaw and Chidchanok Choksuchat
Page: 1195~1214, Vol. 14, No.5, 2018
10.3745/JIPS.04.0088
Keywords: Image Search, Ontology, Semantic Web, Tourism, Western Thailand
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Typical search engines may not be the most efficient means of returning images in accordance with user requirements. With the help of semantic web technology, it is possible to search through images more precisely in any required domain, because the images are annotated according to a custom-built ontology. With appropriate annotations, a search can then, return images according to the context. This paper reports on the design of a tourism ontology relevant to touristic images. In particular, the image features and the meaning of the images are described using various properties, along with other types of information relevant to tourist attractions using the OWL language. The methodology used is described, commencing with building an image and tourism corpus, creating the ontology, and developing the search engine. The system was tested through a case study involving the western region of Thailand. The user can search specifying the specific class of image or they can use text-based searches. The results are ranked using weighted scores based on kinds of properties. The precision and recall of the prototype system was measured to show its efficiency. User satisfaction was also evaluated, was also performed and was found to be high.


A Dependency Graph-Based Keyphrase Extraction Method Using Anti-patterns
Khuyagbaatar Batsuren, Erdenebileg Batbaatar, Tsendsuren Munkhdalai, Meijing Li, Oyun-Erdene Namsrai and Keun Ho Ryu
Page: 1254~1271, Vol. 14, No.5, 2018
10.3745/JIPS.04.0091
Keywords: Dependency Graph, Keyphrase Extraction
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Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many textmining applications such as document summarization and clustering. In this paper, we propose to use two novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share very few similar patterns and stylistic features while non-keyphrase candidates often share many similar patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence graph that could not connect two words that are syntactically related and placed far from each other in a sentence while the dependency graph can do so. In experiments, we have compared the performances with different settings of the graphs (co-occurrence and dependency), and with the existing method results. Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the state-of-the-art performances.


Video Captioning with Visual and Semantic Features
Sujin Lee and Incheol Kim
Page: 1318~1330, Vol. 14, No.6, 2018
10.3745/JIPS.02.0098
Keywords: Attention-Based Caption Generation, Deep Neural Networks, Semantic Feature, Video Captioning
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Video captioning refers to the process of extracting features from a video and generating video captions using the extracted features. This paper introduces a deep neural network model and its learning method for effective video captioning. In this study, visual features as well as semantic features, which effectively express the video, are also used. The visual features of the video are extracted using convolutional neural networks, such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction network proposed in this paper. Further, an attention-based caption generation network is proposed for effective generation of video captions using the extracted features. The performance and effectiveness of the proposed model is verified through various experiments using two large-scale video benchmarks such as the Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).


Triqubit-state Measurement-based Image Edge Detection Algorithm
Zhonghua Wang and Faliang Huang
Page: 1331~1346, Vol. 14, No.6, 2018
10.3745/JIPS.04.0095
Keywords: Edge Detection, Partial Differential Equation, Pixel Saliency, Qubit State, Quantum Measurement
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Aiming at the problem that the gradient-based edge detection operators are sensitive to the noise, causing the pseudo edges, a triqubit-state measurement-based edge detection algorithm is presented in this paper. Combing the image local and global structure information, the triqubit superposition states are used to represent the pixel features, so as to locate the image edge. Our algorithm consists of three steps. Firstly, the improved partial differential method is used to smooth the defect image. Secondly, the triqubit-state is characterized by three elements of the pixel saliency, edge statistical characteristics and gray scale contrast to achieve the defect image from the gray space to the quantum space mapping. Thirdly, the edge image is outputted according to the quantum measurement, local gradient maximization and neighborhood chain code searching. Compared with other methods, the simulation experiments indicate that our algorithm has less pseudo edges and higher edge detection accuracy.


A Multi-Scale Parallel Convolutional Neural Network Based Intelligent Human Identification Using Face Information
Chen Li, Mengti Liang, Wei Song and Ke Xiao
Page: 1494~1507, Vol. 14, No.6, 2018
10.3745/JIPS.02.0103
Keywords: Face Recognition, Intelligent Human Identification, MP-CNN, Robust Feature
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Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.


Gait Recognition Algorithm Based on Feature Fusion of GEI Dynamic Region and Gabor Wavelets
Jun Huang, Xiuhui Wang and Jun Wang
Page: 892~903, Vol. 14, No.4, 2018
10.3745/JIPS.02.0088
Keywords: Gait Recognition, Feature Fusion, Gabor Wavelets, GEI, KPCA
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The paper proposes a novel gait recognition algorithm based on feature fusion of gait energy image (GEI) dynamic region and Gabor, which consists of four steps. First, the gait contour images are extracted through the object detection, binarization and morphological process. Secondly, features of GEI at different angles and Gabor features with multiple orientations are extracted from the dynamic part of GEI, respectively. Then averaging method is adopted to fuse features of GEI dynamic region with features of Gabor wavelets on feature layer and the feature space dimension is reduced by an improved Kernel Principal Component Analysis (KPCA). Finally, the vectors of feature fusion are input into the support vector machine (SVM) based on multi classification to realize the classification and recognition of gait. The primary contributions of the paper are: a novel gait recognition algorithm based on based on feature fusion of GEI and Gabor is proposed; an improved KPCA method is used to reduce the feature matrix dimension; a SVM is employed to identify the gait sequences. The experimental results suggest that the proposed algorithm yields over 90% of correct classification rate, which testify that the method can identify better different human gait and get better recognized effect than other existing algorithms.


Texture Image Retrieval Using DTCWT-SVD and Local Binary Pattern Features
Dayou Jiang and Jongweon Kim
Page: 1628~1639, Vol. 13, No.6, 2017
10.3745/JIPS.02.0077
Keywords: Dual-Tree Complex Wavelet Transform, Image Retrieval, Local Binary Pattern, SVD, Texture Feature
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The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.


A Contour Descriptors-Based Generalized Scheme for Handwritten Odia Numerals Recognition
Tusar Kanti Mishra, Banshidhar Majhi and Ratnakar Dash
Page: 174~183, Vol. 13, No.1, 2017
10.3745/JIPS.02.0012
Keywords: Contour Features, Handwritten Character, Neural Classifier, Numeral Recognition, OCR, Odia
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In this paper, we propose a novel feature for recognizing handwritten Odia numerals. By using polygonal approximation, each numeral is segmented into segments of equal pixel counts where the centroid of the character is kept as the origin. Three primitive contour features namely, distance (l), angle (?), and arc-to- chord ratio (r), are extracted from these segments. These features are used in a neural classifier so that the numerals are recognized. Other existing features are also considered for being recognized in the neural classifier, in order to perform a comparative analysis. We carried out a simulation on a large data set and conducted a comparative analysis with other features with respect to recognition accuracy and time requirements. Furthermore, we also applied the feature to the numeral recognition of two other languages— Bangla and English. In general, we observed that our proposed contour features outperform other schemes.


Fuzzy-Membership Based Writer Identification from Handwritten Devnagari Script
Rajiv Kumar, Kiran Kumar Ravulakollu and Rajesh Bhat
Page: 893~913, Vol. 13, No.4, 2017
10.3745/JIPS.02.0018
Keywords: CPAR-2012, Devnagari, Fuzzy Membership, Handwritten Script, Writer Identification
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The handwriting based person identification systems use their designer’s perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer’s personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.


Combination of Classifiers Decisions for Multilingual Speaker Identification
B. G. Nagaraja and H. S. Jayanna
Page: 928~940, Vol. 13, No.4, 2017
10.3745/JIPS.02.0025
Keywords: Classifier Combination, Cross-lingual, Monolingual, Multilingual, Speaker Identification
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State-of-the-art speaker recognition systems may work better for the English language. However, if the same system is used for recognizing those who speak different languages, the systems may yield a poor performance. In this work, the decisions of a Gaussian mixture model-universal background model (GMM- UBM) and a learning vector quantization (LVQ) are combined to improve the recognition performance of a multilingual speaker identification system. The difference between these classifiers is in their modeling techniques. The former one is based on probabilistic approach and the latter one is based on the fine-tuning of neurons. Since the approaches are different, each modeling technique identifies different sets of speakers for the same database set. Therefore, the decisions of the classifiers may be used to improve the performance. In this study, multitaper mel-frequency cepstral coefficients (MFCCs) are used as the features and the monolingual and cross-lingual speaker identification studies are conducted using NIST-2003 and our own database. The experimental results show that the combined system improves the performance by nearly 10% compared with that of the individual classifier.


Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network
Sanjeev Kumar and Mahesh Chandra
Page: 703~715, Vol. 13, No.4, 2017
10.3745/JIPS.01.0007
Keywords: Cascade-Forward Back Propagation Technique, Computer-Aided Diagnosis (CAD), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gray-Level Co-Occurrence Matrix (GLCM), Mammographic Image Analysis Society (MIAS) Database, Modified Sigmoid Function
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Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray- level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.


An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight
Ingyu Lee and Byungin Moon
Page: 256~267, Vol. 13, No.2, 2017
10.3745/JIPS.02.0057
Keywords: Adaptive Census Transform, Adaptive Support Weight, Local Matching, Multiple Sparse Windows, Stereo Matching
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An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.


Speech Query Recognition for Tamil Language Using Wavelet and Wavelet Packets
P. Iswarya and V. Radha
Page: 1135~1148, Vol. 13, No.5, 2017
10.3745/JIPS.02.0033
Keywords: De-noising, Feature Extraction, Speech Recognition, Support Vector Machine, Wavelet Packet
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Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.


Rough Set-Based Approach for Automatic Emotion Classification of Music
Babu Kaji Baniya and Joonwhoan Lee
Page: 400~416, Vol. 13, No.2, 2017
10.3745/JIPS.04.0032
Keywords: Attributes, Covariance, Discretize, Rough Set, Rules
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Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the 4th order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.


Content-Based Image Retrieval Using Combined Color and Texture Features Extracted by Multi-resolution Multi-direction Filtering
Hee-Hyung Bu, Nam-Chul Kim, Chae-Joo Moon and Jong-Hwa Kim
Page: 464~475, Vol. 13, No.3, 2017
10.3745/JIPS.02.0060
Keywords: Color and Texture Feature, Content-Based Image Retrieval, HSV Color Space, Multi-resolution Multi-direction Filtering
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In this paper, we present a new texture image retrieval method which combines color and texture features extracted from images by a set of multi-resolution multi-direction (MRMD) filters. The MRMD filter set chosen is simple and can be separable to low and high frequency information, and provides efficient multi- resolution and multi-direction analysis. The color space used is HSV color space separable to hue, saturation, and value components, which are easily analyzed as showing characteristics similar to the human visual system. This experiment is conducted by comparing precision vs. recall of retrieval and feature vector dimensions. Images for experiments include Corel DB and VisTex DB; Corel_MR DB and VisTex_MR DB, which are transformed from the aforementioned two DBs to have multi-resolution images; and Corel_MD DB and VisTex_MD DB, transformed from the two DBs to have multi-direction images. According to the experimental results, the proposed method improves upon the existing methods in aspects of precision and recall of retrieval, and also reduces feature vector dimensions.


A Fast Ground Segmentation Method for 3D Point Cloud
Phuong Chu, Seoungjae Cho, Sungdae Sim, Kiho Kwak and Kyungeun Cho
Page: 491~499, Vol. 13, No.3, 2017
10.3745/JIPS.02.0061
Keywords: 3D Point Cloud, Ground Segmentation, Light Detection and Ranging, Start-Ground Point, Threshold Point
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In this study, we proposed a new approach to segment ground and nonground points gained from a 3D laser range sensor. The primary aim of this research was to provide a fast and effective method for ground segmentation. In each frame, we divide the point cloud into small groups. All threshold points and start- ground points in each group are then analyzed. To determine threshold points we depend on three features: gradient, lost threshold points, and abnormalities in the distance between the sensor and a particular threshold point. After a threshold point is determined, a start-ground point is then identified by considering the height difference between two consecutive points. All points from a start-ground point to the next threshold point are ground points. Other points are nonground. This process is then repeated until all points are labelled


Fire Detection Using Multi-Channel Information and Gray Level Co-occurrence Matrix Image Features
Jae-Hyun Jun, Min-Jun Kim, Yong-Suk Jang and Sung-Ho Kim
Page: 590~598, Vol. 13, No.3, 2017
10.3745/JIPS.02.0062
Keywords: Color Features, Fire Detection, Texture Features
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Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We propose an approach to fire detection using an image processing technique. In this paper, we propose a fire detection method using multi- channel information and gray level co-occurrence matrix (GLCM) image features. Multi-channels consist of RGB, YCbCr, and HSV color spaces. The flame color and smoke texture information are used to detect the flames and smoke, respectively. The experimental results show that the proposed method performs better than the previous method in terms of accuracy of fire detection


XSSClassifier: An Efficient XSS Attack Detection Approach Based on Machine Learning Classifier on SNSs
Shailendra Rathore, Pradip Kumar Sharma and Jong Hyuk Park
Page: 1014~1028, Vol. 13, No.4, 2017
10.3745/JIPS.03.0079
Keywords: Cross-Site Scripting Attack Detection, Dataset, JavaScript, Machine Learning Classifier, Social Networking Services
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Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as cross- site scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learning- based approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.


A CTR Prediction Approach for Text Advertising Based on the SAE-LR Deep Neural Network
Zilong Jiang, Shu Gao and Wei Dai
Page: 1052~1070, Vol. 13, No.5, 2017
10.3745/JIPS.02.0069
Keywords: Deep Neural Network, Machine Learning, Text Advertising, SAE-LR
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For the autoencoder (AE) implemented as a construction component, this paper uses the method of greedy layer-by-layer pre-training without supervision to construct the stacked autoencoder (SAE) to extract the abstract features of the original input data, which is regarded as the input of the logistic regression (LR) model, after which the click-through rate (CTR) of the user to the advertisement under the contextual environment can be obtained. These experiments show that, compared with the usual logistic regression model and support vector regression model used in the field of predicting the advertising CTR in the industry, the SAE-LR model has a relatively large promotion in the AUC value. Based on the improvement of accuracy of advertising CTR prediction, the enterprises can accurately understand and have cognition for the needs of their customers, which promotes the multi-path development with high efficiency and low cost under the condition of internet finance.


Extraction of ObjectProperty-UsageMethod Relation from Web Documents
Chaveevan Pechsiri, Sumran Phainoun and Rapeepun Piriyakul
Page: 1103~1125, Vol. 13, No.5, 2017
10.3745/JIPS.04.0046
Keywords: Medicinal Property, N-Word-Co, Semantic Relation, Usage-Method
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This paper aims to extract an ObjectProperty-UsageMethod relation, in particular the HerbalMedicinalProperty- UsageMethod relation of the herb-plant object, as a semantic relation between two related sets, a herbal- medicinal-property concept set and a usage-method concept set from several web documents. This HerbalMedicinalProperty-UsageMethod relation benefits people by providing an alternative treatment/solution knowledge to health problems. The research includes three main problems: how to determine EDU (where EDU is an elementary discourse unit or a simple sentence/clause) with a medicinal-property/usage-method concept; how to determine the usage-method boundary; and how to determine the HerbalMedicinalProperty- UsageMethod relation between the two related sets. We propose using N-Word-Co on the verb phrase with the medicinal-property/usage-method concept to solve the first and second problems where the N-Word-Co size is determined by the learning of maximum entropy, support vector machine, and nai?ve Bayes. We also apply nai?ve Bayes to solve the third problem of determining the HerbalMedicinalProperty-UsageMethod relation with N-Word-Co elements as features. The research results can provide high precision in the HerbalMedicinalProperty-UsageMethod relation extraction.


Thai Classical Music Matching using t-Distribution on Instantaneous Robust Algorithm for Pitch Tracking Framework
Pheerasut Boonmatham, Sunee Pongpinigpinyo and Tasanawan Soonklang
Page: 1213~1228, Vol. 13, No.5, 2017
10.3745/JIPS.02.0073
Keywords: Pitch Tracking Algorithm, Instantaneous Robust Algorithm for Pitch Tracking, T-Distribution, Shortest Query Sample
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The pitch tracking of music has been researched for several decades. Several possible improvements are available for creating a good t-distribution, using the instantaneous robust algorithm for pitch tracking framework to perfectly detect pitch. This article shows how to detect the pitch of music utilizing an improved detection method which applies a statistical method; this approach uses a pitch track, or a sequence of frequency bin numbers. This sequence is used to create an index that offers useful features for comparing similar songs. The pitch frequency spectrum is extracted using a modified instantaneous robust algorithm for pitch tracking (IRAPT) as a base combined with the statistical method. The pitch detection algorithm was implemented, and the percentage of performance matching in Thai classical music was assessed in order to test the accuracy of the algorithm. We used the longest common subsequence to compare the similarities in pitch sequence alignments in the music. The experimental results of this research show that the accuracy of retrieval of Thai classical music using the t-distribution of instantaneous robust algorithm for pitch tracking (t-IRAPT) is 99.01%, and is in the top five ranking, with the shortest query sample being five seconds long.


Content-based Image Retrieval Using Texture Features Extracted from Local Energy and Local Correlation of Gabor Transformed Images
Hee-Hyung Bu, Nam-Chul Kim, Bae-Ho Lee and Sung-Ho Kim
Page: 1372~1381, Vol. 13, No.5, 2017
10.3745/JIPS.02.0075
Keywords: Content-based Image Retrieval, Gabor Transformation, Local Energy, Local Correlation, Texture Feature
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In this paper, a texture feature extraction method using local energy and local correlation of Gabor transformed images is proposed and applied to an image retrieval system. The Gabor wavelet is known to be similar to the response of the human visual system. The outputs of the Gabor transformation are robust to variants of object size and illumination. Due to such advantages, it has been actively studied in various fields such as image retrieval, classification, analysis, etc. In this paper, in order to fully exploit the superior aspects of Gabor wavelet, local energy and local correlation features are extracted from Gabor transformed images and then applied to an image retrieval system. Some experiments are conducted to compare the performance of the proposed method with those of the conventional Gabor method and the popular rotation-invariant uniform local binary pattern (RULBP) method in terms of precision vs recall. The Mahalanobis distance is used to measure the similarity between a query image and a database (DB) image. Experimental results for Corel DB and VisTex DB show that the proposed method is superior to the conventional Gabor method. The proposed method also yields precision and recall 6.58% and 3.66% higher on average in Corel DB, respectively, and 4.87% and 3.37% higher on average in VisTex DB, respectively, than the popular RULBP method.


A Novel Statistical Feature Selection Approach for Text Categorization
Mohamed Abdel Fattah
Page: 1397~1409, Vol. 13, No.5, 2017
10.3745/JIPS.02.0076
Keywords: Electronic Texts, E-mail Filtering, Feature Selection, SMS Spam Filtering, Text Categorization
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For text categorization task, distinctive text features selection is important due to feature space high dimensionality. It is important to decrease the feature space dimension to decrease processing time and increase accuracy. In the current study, for text categorization task, we introduce a novel statistical feature selection approach. This approach measures the term distribution in all collection documents, the term distribution in a certain category and the term distribution in a certain class relative to other classes. The proposed method results show its superiority over the traditional feature selection methods.


Mitigating Threats and Security Metrics in Cloud Computing
Jayaprakash Kar and Manoj Ranjan Mishra
Page: 226~233, Vol. 12, No.2, 2016
10.3745/JIPS.03.0049
Keywords: Dynamic Access Control, Risk Assessment, Security Intelligence
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Cloud computing is a distributed computing model that has lot of drawbacks and faces difficulties. Many new innovative and emerging techniques take advantage of its features. In this paper, we explore the security threats to and Risk Assessments for cloud computing, attack mitigation frameworks, and the risk-based dynamic access control for cloud computing. Common security threats to cloud computing have been explored and these threats are addressed through acceptable measures via governance and effective risk management using a tailored Security Risk Approach. Most existing Threat and Risk Assessment (TRA) schemes for cloud services use a converse thinking approach to develop theoretical solutions for minimizing the risk of security breaches at a minimal cost. In our study, we propose an improved Attack-Defense Tree mechanism designated as iADTree, for solving the TRA problem in cloud computing environments.


Two-Dimensional Joint Bayesian Method for Face Verification
Sunghyu Han, Il-Yong Lee and Jung-Ho Ahn
Page: 381~391, Vol. 12, No.3, 2016
10.3745/JIPS.02.0036
Keywords: Face Verification, Joint Bayesian Method, LBP, LFW Database, Two-Dimensional Joint Bayesian Method
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The Joint Bayesian (JB) method has been used in most state-of-the-art methods for face verification. However, since the publication of the original JB method in 2012, no improved verification method has been proposed. A lot of studies on face verification have been focused on extracting good features to improve the performance in the challenging Labeled Faces in the Wild (LFW) database. In this paper, we propose an improved version of the JB method, called the two-dimensional Joint Bayesian (2D-JB) method. It is very simple but effective in both the training and test phases. We separated two symmetric terms from the three terms of the JB log likelihood ratio function. Using the two terms as a two-dimensional vector, we learned a decision line to classify same and not-same cases. Our experimental results show that the proposed 2D-JB method significantly outperforms the original JB method by more than 1% in the LFW database.


ELPA: Emulation-Based Linked Page Map Analysis for the Detection of Drive-by Download Attacks
Sang-Yong Choi, Daehyeok Kim and Yong-Min Kim
Page: 422~435, Vol. 12, No.3, 2016
10.3745/JIPS.03.0045
Keywords: Drive-by Download, Malware Distribution Network, Webpage Link Analysis, Web Security
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Despite the convenience brought by the advances in web and Internet technology, users are increasingly being exposed to the danger of various types of cyber attacks. In particular, recent studies have shown that today’s cyber attacks usually occur on the web via malware distribution and the stealing of personal information. A drive-by download is a kind of web-based attack for malware distribution. Researchers have proposed various methods for detecting a drive-by download attack effectively. However, existing methods have limitations against recent evasion techniques, including JavaScript obfuscation, hiding, and dynamic code evaluation. In this paper, we propose an emulation-based malicious webpage detection method. Based on our study on the limitations of the existing methods and the state-of-the-art evasion techniques, we will introduce four features that can detect malware distribution networks and we applied them to the proposed method. Our performance evaluation using a URL scan engine provided by VirusTotal shows that the proposed method detects malicious webpages more precisely than existing solutions.


Evaluation of Histograms Local Features and Dimensionality Reduction for 3D Face Verification
Chouchane Ammar*, Belahcene Mebarka, Ouamane Abdelmalik and Bourennane Salah
Page: 468~488, Vol. 12, No.3, 2016
10.3745/JIPS.02.0037
Keywords: 3D Face Verification, Depth Image, Dimensionality Reduction, Histograms Local Features, Local Descriptors, Support Vector Machine
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The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four- Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.


A Multiple Features Video Copy Detection Algorithm Based on a SURF Descriptor
Yanyan Hou, Xiuzhen Wang and Sanrong Liu
Page: 502~510, Vol. 12, No.3, 2016
10.3745/JIPS.02.0042
Keywords: Local Invariant Feature, Speeded-Up Robust Features, Video Copy Detection
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Considering video copy transform diversity, a multi-feature video copy detection algorithm based on a Speeded-Up Robust Features (SURF) local descriptor is proposed in this paper. Video copy coarse detection is done by an ordinal measure (OM) algorithm after the video is preprocessed. If the matching result is greater than the specified threshold, the video copy fine detection is done based on a SURF descriptor and a box filter is used to extract integral video. In order to improve video copy detection speed, the Hessian matrix trace of the SURF descriptor is used to pre-match, and dimension reduction is done to the traditional SURF feature vector for video matching. Our experimental results indicate that video copy detection precision and recall are greatly improved compared with traditional algorithms, and that our proposed multiple features algorithm has good robustness and discrimination accuracy, as it demonstrated that video detection speed was also improved.


SDN-Based Enterprise and Campus Networks: A Case of VLAN Management
Van-Giang Nguyen and Young-Han Kim
Page: 511~524, Vol. 12, No.3, 2016
10.3745/JIPS.03.0039
Keywords: Campus Network, Enterprise Network, OpenFlow, Software Defined Networking (SDN), VLAN Management
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The Virtual Local Area Network (VLAN) has been used for a long time in campus and enterprise networks as the most popular network virtualization solution. Due to the benefits and advantages achieved by using VLAN, network operators and administrators have been using it for constructing their networks up until now and have even extended it to manage the networking in a cloud computing system. However, their configuration is a complex, tedious, time-consuming, and error-prone process. Since Software Defined Networking (SDN) features the centralized network management and network programmability, it is a promising solution for handling the aforementioned challenges in VLAN management. In this paper, we first introduce a new architecture for campus and enterprise networks by leveraging SDN and OpenFlow. Next, we have designed and implemented an application for easily managing and flexibly troubleshooting the VLANs in this architecture. This application supports both static VLAN and dynamic VLAN configurations. In addition, we discuss the hybrid-mode operation where the packet processing is involved by both the OpenFlow control plane and the traditional control plane. By deploying a real test-bed prototype, we illustrate how our system works and then evaluate the network latency in dynamic VLAN operation.


Homogeneous and Non-homogeneous Polynomial Based Eigenspaces to Extract the Features on Facial Images
Arif Muntasa
Page: 591~611, Vol. 12, No.4, 2016
10.3745/JIPS.01.0011
Keywords: Eigenspaces, Feature Extraction, Homogeneous, Non-homogeneous
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High dimensional space is the biggest problem when classification process is carried out, because it takes longer time for computation, so that the costs involved are also expensive. In this research, the facial space generated from homogeneous and non-homogeneous polynomial was proposed to extract the facial image features. The homogeneous and non-homogeneous polynomial-based eigenspaces are the second opinion of the feature extraction of an appearance method to solve non-linear features. The kernel trick has been used to complete the matrix computation on the homogeneous and non-homogeneous polynomial. The weight and projection of the new feature space of the proposed method have been evaluated by using the three face image databases, i.e., the YALE, the ORL, and the UoB. The experimental results have produced the highest recognition rate 94.44%, 97.5%, and 94% for the YALE, ORL, and UoB, respectively. The results explain that the proposed method has produced the higher recognition than the other methods, such as the Eigenface, Fisherface, Laplacianfaces, and O-Laplacianfaces


Image Deblocking Scheme for JPEG Compressed Images Using an Adaptive-Weighted Bilateral Filter
Liping Wang, Chengyou Wang, Wei Huang and Xiao Zhou
Page: 631~643, Vol. 12, No.4, 2016
10.3745/JIPS.02.0046
Keywords: Image Deblocking, Adaptive-Weighted Bilateral Filter, Blind Image Quality Assessment (BIQA), Local Entropy
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Due to the block-based discrete cosine transform (BDCT), JPEG compressed images usually exhibit blocking artifacts. When the bit rates are very low, blocking artifacts will seriously affect the image’s visual quality. A bilateral filter has the features for edge-preserving when it smooths images, so we propose an adaptiveweighted bilateral filter based on the features. In this paper, an image-deblocking scheme using this kind of adaptive-weighted bilateral filter is proposed to remove and reduce blocking artifacts. Two parameters of the proposed adaptive-weighted bilateral filter are adaptive-weighted so that it can avoid over-blurring unsmooth regions while eliminating blocking artifacts in smooth regions. This is achieved in two aspects: by using local entropy to control the level of filtering of each single pixel point within the image, and by using an improved blind image quality assessment (BIQA) to control the strength of filtering different images whose blocking artifacts are different. It is proved by our experimental results that our proposed image-deblocking scheme provides good performance on eliminating blocking artifacts and can avoid the over-blurring of unsmooth regions


A Chi-Square-Based Decision for Real-Time Malware Detection Using PE-File Features
Mohamed Belaoued and Smaine Mazouzi
Page: 644~660, Vol. 12, No.4, 2016
10.3745/JIPS.03.0058
Keywords: Chi-Square Test, Malware Analysis, PE-Optional Header, Real-Time Detection Windows API
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The real-time detection of malware remains an open issue, since most of the existing approaches for malware categorization focus on improving the accuracy rather than the detection time. Therefore, finding a proper balance between these two characteristics is very important, especially for such sensitive systems. In this paper, we present a fast portable executable (PE) malware detection system, which is based on the analysis of the set of Application Programming Interfaces (APIs) called by a program and some technical PE features (TPFs). We used an efficient feature selection method, which first selects the most relevant APIs and TPFs using the chi-square (KHI²) measure, and then the Phi (?) coefficient was used to classify the features in different subsets, based on their relevance. We evaluated our method using different classifiers trained on different combinations of feature subsets. We obtained very satisfying results with more than 98% accuracy. Our system is adequate for real-time detection since it is able to categorize a file (Malware or Benign) in 0.09 seconds


A Robust Fingerprint Matching System Using Orientation Features
Ravinder Kumar, Pravin Chandra and Madasu Hanmandlu
Page: 83~99, Vol. 12, No.1, 2016
10.3745/JIPS.02.0020
Keywords: Circular ROI, Core Point Detection, Image-Based Fingerprint Matching, Orientation Features
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The latest research on the image-based fingerprint matching approaches indicates that they are less complex than the minutiae-based approaches when it comes to dealing with low quality images. Most of the approaches in the literature are not robust to fingerprint rotation and translation. In this paper, we develop a robust fingerprint matching system by extracting the circular region of interest (ROI) of a radius of 50 pixels centered at the core point. Maximizing their orientation correlation aligns two fingerprints that are to be matched. The modified Euclidean distance computed between the extracted orientation features of the sample and query images is used for matching. Extensive experiments were conducted over four benchmark fingerprint datasets of FVC2002 and two other proprietary databases of RFVC 2002 and the AITDB. The experimental results show the superiority of our proposed method over the well-known image-based approaches in the literature.


Blind Color Image Watermarking Based on DWT and LU Decomposition
Dongyan Wang, Fanfan Yang and Heng Zhang
Page: 765~778, Vol. 12, No.4, 2016
10.3745/JIPS.03.0055
Keywords: Digital Color Image Watermark, Discrete Wavelet Transformation (DWT), LU Decomposition, Normalized Correlation (NC), Structural Similarity (SSIM)
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In watermarking schemes, the discrete wavelet transform (DWT) is broadly used because its frequency component separation is very useful. Moreover, LU decomposition has little influence on the visual quality of the watermark. Hence, in this paper, a novel blind watermark algorithm is presented based on LU transform and DWT for the copyright protection of digital images. In this algorithm, the color host image is first performed with DWT. Then, the horizontal and vertical diagonal high frequency components are extracted from the wavelet domain, and the sub-images are divided into 4×4 non-overlapping image blocks. Next, each sub-block is performed with LU decomposition. Finally, the color image watermark is transformed by Arnold permutation, and then it is inserted into the upper triangular matrix. The experimental results imply that this algorithm has good features of invisibility and it is robust against different attacks to a certain degree, such as contrast adjustment, JPEG compression, salt and pepper noise, cropping, and Gaussian noise


Analysis of Semantic Relations Between Multimodal Medical Images Based on Coronary Anatomy for Acute Myocardial Infarction
Yeseul Park, Meeyeon Lee, Myung-Hee Kim and Jung-Won Lee
Page: 129~148, Vol. 12, No.1, 2016
10.3745/JIPS.04.0021
Keywords: Acute Myocardial Infarction, Coronary Anatomy, Coronary Angiography, Data Model, Echocardiography, Medical Images, Multimodality, Semantic Features
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Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multi- modal medical images with flat and unstructured data. It has a lack of semantic information between multi- modal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively.


GMM-Based Maghreb Dialect IdentificationSystem
Lachachi Nour-Eddine and Adla Abdelkader
Page: 22~38, Vol. 11, No.1, 2015
10.3745/JIPS.02.0015
Keywords: Core-Set, Gaussian Mixture Models (GMM), Kernel Methods, Minimal Enclosing Ball (MEB), Quadratic Programming (QP), Support Vector Machines (SVMs), Universal Background Model (UBM)
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While Modern Standard Arabic is the formal spoken and written language of the Arab world; dialects are the major communication mode for everyday life. Therefore, identifying a speaker’s dialect is critical in the Arabic-speaking world for speech processing tasks, such as automatic speech recognition or identification. In this paper, we examine two approaches that reduce the Universal Background Model (UBM) in the automatic dialect identification system across the five following Arabic Maghreb dialects: Moroccan, Tunisian, and 3 dialects of the western (Oranian), central (Algiersian), and eastern (Constantinian) regions of Algeria. We applied our approaches to the Maghreb dialect detection domain that contains a collection of 10-second utterances and we compared the performance precision gained against the dialect samples from a baseline GMM-UBM system and the ones from our own improved GMM-UBM system that uses a Reduced UBM algorithm. Our experiments show that our approaches significantly improve identification performance over purely acoustic features with an identification rate of 80.49%.


Simple Pyramid RAM-Based Neural Network Architecture for Localization of Swarm Robots
Siti Nurmaini and Ahmad Zarkasi
Page: 370~388, Vol. 11, No.3, 2015
10.3745/JIPS.01.0008
Keywords: Localization Process, RAM-Based Neural Network, Swarm Robots
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The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent’s position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.


Robust ROI Watermarking Scheme Based on Visual Cryptography: Application on Mammograms
Meryem Benyoussef, Samira Mabtoul, Mohamed El Marraki and Driss Aboutajdine
Page: 495~508, Vol. 11, No.4, 2015
10.3745/JIPS.02.0032
Keywords: Copyright Protection, Mammograms, Medical Image, Robust Watermarking, Visual Cryptography
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In this paper, a novel robust medical images watermarking scheme is proposed. In traditional methods, the added watermark may alter the host medical image in an irreversible manner and may mask subtle details. Consequently, we propose a method for medical image copyright protection that may remedy this problem by embedding the watermark without modifying the original host image. The proposed method is based on the visual cryptography concept and the dominant blocks of wavelet coefficients. The logic in using the blocks dominants map is that local features, such as contours or edges, are unique to each image. The experimental results show that the proposed method can withstand several image processing attacks such as cropping, filtering, compression, etc.


Event Detection on Motion Activities Using a Dynamic Grid
Jitdumrong Preechasuk and Punpiti Piamsa-nga
Page: 538~555, Vol. 11, No.4, 2015
10.3745/JIPS.02.0035
Keywords: Dynamic Grid Feature, Event Detection, Event Patterns, Pedestrian Activities
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Event detection based on using features from a static grid can give poor results from the viewpoint of two main aspects: the position of the camera and the position of the event that is occurring in the scene. The former causes problems when training and test events are at different distances from the camera to the actual position of the event. The latter can be a source of problems when training events take place in any position in the scene, and the test events take place in a position different from the training events. Both issues degrade the accuracy of the static grid method. Therefore, this work proposes a method called a dynamic grid for event detection, which can tackle both aspects of the problem. In our experiment, we used the dynamic grid method to detect four types of event patterns: implosion, explosion, two-way, and one-way using a Multimedia Analysis and Discovery (MAD) pedestrian dataset. The experimental results show that the proposed method can detect the four types of event patterns with high accuracy. Additionally, the performance of the proposed method is better than the static grid method and the proposed method achieves higher accuracy than the previous method regarding the aforementioned aspects.


Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition
Deepak Ghimire and Joonwhoan Lee
Page: 443~458, Vol. 10, No.3, 2014
10.3745/JIPS.02.0004
Keywords: Bagging, Ensemble Learning, Extreme Learning Machine, Facial Expression Recognition, Histogram of Orientation Gradient
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An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.


Graphemes Segmentation for Arabic Online Handwriting Modeling
Houcine Boubaker, Najiba Tagougui, Haikal El Abed, Monji Kherallah and Adel M. Alimi
Page: 503~522, Vol. 10, No.4, 2014
10.3745/JIPS.02.0006
Keywords: Baseline Detection, Diacritic Features, Fourier Descriptors, Geometric Parameters, Grapheme Segmentation, Online Arabic Handwriting Modeling
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In the cursive handwriting recognition process, script trajectory segmentation and modeling represent an important task for large or open lexicon context that becomes more complicated in multi-writer applications. In this paper, we will present a developed system of Arabic online handwriting modeling based on graphemes segmentation and the extraction of its geometric features. The main contribution consists of adapting the Fourier descriptors to model the open trajectory of the segmented graphemes. To segment the trajectory of the handwriting, the system proceeds by first detecting its baseline by checking combined geometric and logic conditions. Then, the detected baseline is used as a topologic reference for the extraction of particular points that delimit the graphemes’ trajectories. Each segmented grapheme is then represented by a set of relevant geometric features that include the vector of the Fourier descriptors for trajectory shape modeling, normalized metric parameters that model the grapheme dimensions, its position in respect to the baseline, and codes for the description of its associated diacritics.


A TRUS Prostate Segmentation using Gabor Texture Features and Snake-like Contour
Sung Gyun Kim and Yeong Geon Seo
Page: 103~116, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.103
Keywords: Gabor Filter Bank, Support Vector Machines, Prostate Segmentation
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Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound(TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation in TRUS images using Gabor feature extraction and snake-like contour is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. The speckle reduction for preprocessing step has been achieved by using stick filter and top-hat transform has been implemented for smoothing the contour. A Gabor filter bank for extraction of rotation- invariant texture features has been implemented. A support vector machine(SVM) for training step has been used to get each feature of prostate and nonprostate. Finally, the boundary of prostate is extracted by the snake-like contour algorithm. A number of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with less than 10.2% of the accuracy which is relative to boundary provided manually by experts


Optical Character Recognition for Hindi Language Using a Neural-network Approach
Divakar Yadav, Sonia Sánchez-Cuadrado and Jorge Morato
Page: 117~140, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.117
Keywords: OCR, Pre-processing, Segmentation, Feature Vector, Classification, Artificial Neural Network (ANN)
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Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document"'"s textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.


A Robust Face Detection Method Based on Skin Color and Edges
Deepak Ghimire and Joonwhoan Lee
Page: 141~156, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.141
Keywords: Face Detection, Image Enhancement, Skin Tone Percentage Index, Canny Edge, Facial Features
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In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non- face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions


Region-Based Facial Expression Recognition in Still Images
Gawed M. Nagi, Rahmita Rahmat, Fatimah Khalid and Muhamad Taufik
Page: 173~188, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.173
Keywords: Facial Expression Recognition (FER), Facial Features Detection, Facial Features Extraction, Cascade Classifier, LBP, One-Vs-Rest SVM
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In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.


Modified Multi-Chaotic Systems that are Based on Pixel Shuffle for Image Encryption
Om Prakash Verma, Munazza Nizam and Musheer Ahmad
Page: 271~286, Vol. 9, No.2, 2013
10.3745/JIPS.2013.9.2.271
Keywords: Chaotic Systems, Number of Pixel Change Rate, Unified Average Changed Intensity, Correlation Coefficient, Entropy
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Recently, a pixel-chaotic-shuffling (PCS) method has been proposed by Huang et al. for encrypting color images using multiple chaotic systems like the Henon, the Lorenz, the Chua, and the Rossler systems. All of which have great encryption performance. The authors claimed that their pixel-chaotic-shuffle (PCS) encryption method has high confidential security. However, the security analysis of the PCS method against the chosen-plaintext attack (CPA) and known-plaintext attack (KPA) performed by Solak et al. successfully breaks the PCS encryption scheme without knowing the secret key. In this paper we present an improved shuffling pattern for the plaintext image bits to make the cryptosystem proposed by Huang et al. resistant to chosen-plaintext attack and known-plaintext attack. The modifications in the existing PCS encryption method are proposed to improve its security performance against the potential attacks described above. The Number of Pixel Change Rate (NPCR), Unified Average Changed Intensity (UACI), information entropy, and correlation coefficient analysis are performed to evaluate the statistical performance of the modified PCS method. The simulation analysis reveals that the modified PCS method has better statistical features and is more resistant to attacks than Huang et al.’s PCS method.


Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer
Thang Hoang, Thuc Nguyen, Chuyen Luong, Son Do and Deokjai Choi
Page: 333~348, Vol. 9, No.2, 2013
10.3745/JIPS.2013.9.2.333
Keywords: Gait Recognition, Mobile Security, Accelerometer, Pattern Recognition, Authentication, Identification, Signal Processing
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Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately 91.33 ± 0.67 % for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.


Interactive Semantic Image Retrieval
Pushpa B. Patil and Manesh B. Kokare
Page: 349~364, Vol. 9, No.3, 2013
10.3745/JIPS.2013.9.3.349
Keywords: Content-based Image Retrieval (CBIR), Relevance Feedback (RF), Rotated Complex Wavelet Filt ers (RCWFs), Dual Tree Complex Wavelet, and Image retrieval
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The big challenge in current content-based image retrieval systems is to reduce the semantic gap between the low level-features and high-level concepts. In this paper, we have proposed a novel framework for efficient image retrieval to improve the retrieval results significantly as a means to addressing this problem. In our proposed method, we first extracted a strong set of image features by using the dual-tree rotated complex wavelet filters (DT-RCWF) and dual tree-complex wavelet transform (DT-CWT) jointly, which obtains features in 12 different directions. Second, we presented a relevance feedback (RF) framework for efficient image retrieval by employing a support vector machine (SVM), which learns the semantic relationship among images using the knowledge, based on the user interaction. Extensive experiments show that there is a significant improvement in retrieval performance with the proposed method using SVMRF compared with the retrieval performance without RF. The proposed method improves retrieval p erformance from 78.5% to 92.29% on the texture database in terms of retrieval accuracy and from 57.20% to 94.2% on the Corel image database, in terms of precision in a much lower number of iterations.


Classifying Malicious Web Pages by Using an Adaptive Support Vector Machine
Young Sup Hwang, Jin Baek Kwon, Jae Chan Moon and Seong Je Cho
Page: 395~404, Vol. 9, No.3, 2013
10.3745/JIPS.2013.9.3.395
Keywords: adaptive classification, malicious web pages, support vector machine
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In order to classify a web page as being benign or malicious, we designed 14 basic and 16 extended features. The basic features that we implemented were selected to represent the essential characteristics of a web page. The system heuristically combines two basic features into one extended feature in order to effectively distinguish benign and malicious pages. The support vector machine can be trained to successfully classify pages by using these features. Because more and more malicious web pages are appearing, and they change so rapidly, classifiers that are trained by old data may misclassify some new pages. To overcome this problem, we selected an adaptive support vector machine (aSVM) as a classifier. The aSVM can learn training data and can quickly learn additional training data based on the support vectors it obtained during its previous learning session. Experimental results verified that the aSVM can classify malicious web pages adaptively.


A Simulation Model of Object Movement for Evaluating the Communication Load in Networked Virtual Environments
Mingyu Lim and Yunjin Lee
Page: 489~498, Vol. 9, No.3, 2013
10.3745/JIPS.2013.9.3.489
Keywords: Networked Virtual Environments, Simulation Model, Load Distribution, Interest Management
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In this paper, we propose a common simulation model that can be reused for different performance evaluations of networked virtual environments. To this end, we analyzed the common features of NVEs, in which multiple regions compose a shared space, and where a user has his/her own interest area. Communication architecture can be client-server or peer-server models. In usual simulations, users move around the world while the number of users varies with the system. Our model provides various simulation parameters to customize the region configuration and user movement pattern. Furthermore, our model introduces a way to mimic a lot of users in a minimal experiment environment. The proposed model is integrated with our network framework, which supports various scalability approaches. We specifically applied our model to the interest management and load distribution schemes to evaluate communication overhead. With the proposed simulation model, a new simulation can be easily designed in a large-scale environment.


Opinion Bias Detection Based on Social Opinions for Twitter
A-Rong Kwon and Kyung-Soon Lee
Page: 538~547, Vol. 9, No.4, 2013
10.3745/JIPS.2013.9.4.538
Keywords: Social opinion, Personal opinion, Bias detection, Sentiment, Target
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In this paper, we propose a bias detection method that is based on personal and social opinions that express contrasting views on competing topics on Twitter. We used unsupervised polarity classification is conducted for learning social opinions on targets. The tf-idf algorithm is applied to extract targets to reflect sentiments and features of tweets. Our method addresses there being a lack of a sentiment lexicon when learning social opinions. To evaluate the effectiveness of our method, experiments were conducted on four issues using Twitter test collection. The proposed method achieved significant improvements over the baselines.


Supporting Java Components in the SID Simulation System
Hasrul Ma'ruf, Hidayat Febiansyah and Jin Baek Kwon
Page: 101~118, Vol. 8, No.1, 2012
10.3745/JIPS.2011.8.1.101
Keywords: Embedded System, Simulation System, SID Simulator
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Embedded products are becoming richer in features. Simulation tools facilitate low-costs and the efficient development of embedded systems. SID is an open source simulation software that includes a library of components for modeling hardware and software components. SID components were originally written using C/C++ and Tcl/Tk. Tcl/Tk has mainly been used for GUI simulation in the SID system. However, Tcl/Tk components are hampered by low performance, and GUI development using Tcl/Tk also has poor flexibility. Therefore, it would be desirable to use a more advanced programming language, such as Java, to provide simulations of cutting-edge products with rich graphics. Here, we describe the development of the Java Bridge Module as a middleware that will enable the use of Java Components in SID. We also extended the low-level SID API to Java. In addition, we have added classes that contain default implementations of the API. These classes are intended to ensure the compatibility and simplicity of SID components in Java.


The Use of MSVM and HMM for Sentence Alignment
Mohamed Abdel Fattah
Page: 301~314, Vol. 8, No.2, 2012
10.3745/JIPS.2012.8.2.301
Keywords: Sentence Alignment, English/ Arabic Parallel Corpus, Parallel Corpora, Machine Translation, Multi-Class Support Vector Machine, Hidden Markov model
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In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research


Texture Comparison with an Orientation Matching Scheme
Nguyen Cao Truong Hai, Do-Yeon Kim and Hyuk-Ro Park
Page: 389~398, Vol. 8, No.3, 2012
10.3745/JIPS.2012.8.3.389
Keywords: Orientation Matching, Texture Analysis, Texture Comparison, K-means Clustering
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Texture is an important visual feature for image analysis. Many approaches have been proposed to model and analyze texture features. Although these approaches significantly contribute to various image-based applications, most of these methods are sensitive to the changes in the scale and orientation of the texture pattern. Because textures vary in scale and orientations frequently, this easily leads to pattern mismatching if the features are compared to each other without considering the scale and/or orientation of textures. This paper suggests an Orientation Matching Scheme (OMS) to ease the problem of mismatching rotated patterns. In OMS, a pair of texture features will be compared to each other at various orientations to identify the best matched direction for comparison. A database including rotated texture images was generated for experiments. A synthetic retrieving experiment was conducted on the generated database to examine the performance of the proposed scheme. We also applied OMS to the similarity computation in a K-means clustering algorithm. The purpose of using K-means is to examine the scheme exhaustively in unpromising conditions, where initialized seeds are randomly selected and algorithms work heuristically. Results from both types of experiments show that the proposed OMS can help improve the performance when dealing with rotated patterns.


Iris Recognition Using Ridgelets
Lenina Birgale and Manesh Kokare
Page: 445~458, Vol. 8, No.3, 2012
10.3745/JIPS.2012.8.3.445
Keywords: Ridgelets, Texture, Wavelets, Biometrics, Features, Database
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Image feature extraction is one of the basic works for biometric analysis. This paper presents the novel concept of application of ridgelets for iris recognition systems. Ridgelet transforms are the combination of Radon transforms and Wavelet transforms. They are suitable for extracting the abundantly present textural data that is in an iris. The technique proposed here uses the ridgelets to form an iris signature and to represent the iris. This paper contributes towards creating an improved iris recognition system. There is a reduction in the feature vector size, which is 1X4 in size. The False Acceptance Rate (FAR) and False Rejection Rate (FRR) were also reduced and the accuracy increased. The proposed method also avoids the iris normalization process that is traditionally used in iris recognition systems. Experimental results indicate that the proposed method achieves an accuracy of 99.82%, 0.1309% FAR, and 0.0434% FRR.


Designing an Efficient and Secure Credit Cardbased Payment System with Web Services Based on the ANSI X9.59-2006
Chi Po Cheong, Simon Fong, Pouwan Lei, Chris Chatwin and Rupert Young
Page: 495~520, Vol. 8, No.3, 2012
10.3745/JIPS.2012.8.3.495
Keywords: Payment Protocols, Electronic Commerce, SET, X9.59, Web Services
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A secure Electronic Payment System (EPS) is essential for the booming online shopping market. A successful EPS supports the transfer of electronic money and sensitive information with security, accuracy, and integrity between the seller and buyer over the Internet. SET, CyberCash, Paypal, and iKP are the most popular Credit Card- Based EPSs (CCBEPSs). Some CCBEPSs only use SSL to provide a secure communication channel. Hence, they only prevent “Man in the Middle” fraud but do not protect the sensitive cardholder information such as the credit card number from being passed onto the merchant, who may be unscrupulous. Other CCBEPSs use complex mechanisms such as cryptography, certificate authorities, etc. to fulfill the security schemes. However, factors such as ease of use for the cardholder and the implementation costs for each party are frequently overlooked. In this paper, we propose a Web service based new payment system, based on ANSI X9.59-2006 with extra features added on top of this standard. X9.59 is an Account Based Digital Signature (ABDS) and consumeroriented payment system. It utilizes the existing financial network and financial messages to complete the payment process. However, there are a number of limitations in this standard. This research provides a solution to solve the limitations of X9.59 by adding a merchant authentication feature during the payment cycle without any addenda records to be added in the existing financial messages. We have conducted performance testing on the proposed system via a comparison with SET and X9.59 using simulation to analyze their levels of performance and security.


Online Recognition of Handwritten Korean and English Characters
Ming Ma, Dong-Won Park, Soo Kyun Kim and Syungog An
Page: 653~668, Vol. 8, No.4, 2012
10.3745/JIPS.2012.8.4.653
Keywords: Online Handwriting Recognition, Hidden Markov Model, Stochastic Grammar, Hierarchical Clustering, Position Verifier
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In this study, an improved HMM based recognition model is proposed for online English and Korean handwritten characters. The pattern elements of the handwriting model are sub character strokes and ligatures. To deal with the problem of handwriting style variations, a modified Hierarchical Clustering approach is introduced to partition different writing styles into several classes. For each of the English letters and each primitive grapheme in Korean characters, one HMM that models the temporal and spatial variability of the handwriting is constructed based on each class. Then the HMMs of Korean graphemes are concatenated to form the Korean character models. The recognition of handwritten characters is implemented by a modified level building algorithm, which incorporates the Korean character combination rules within the efficient network search procedure. Due to the limitation of the HMM based method, a postprocessing procedure that takes the global and structural features into account is proposed. Experiments showed that the proposed recognition system achieved a high writer independent recognition rate on unconstrained samples of both English and Korean characters. The comparison with other schemes of HMM-based recognition was also performed to evaluate the system


Virus Detection Method based on Behavior Resource Tree
Mengsong Zou, Lansheng Han, Ming Liu and Qiwen Liu
Page: 173~186, Vol. 7, No.1, 2011
10.3745/JIPS.2011.7.1.173
Keywords: Computer Virus, Behavior-Based Detection, Dynamic Link Library, Behavior Resource Tree
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Due to the disadvantages of signature-based computer virus detection techniques, behavior-based detection methods have developed rapidly in recent years. However, current popular behavior-based detection methods only take API call sequences as program behavior features and the difference between API calls in the detection is not taken into consideration. This paper divides virus behaviors into separate function modules by introducing DLLs into detection. APIs in different modules have different importance. DLLs and APIs are both considered program calling resources. Based on the calling relationships between DLLs and APIs, program calling resources can be pictured as a tree named program behavior resource tree. Important block structures are selected from the tree as program behavior features. Finally, a virus detection model based on behavior the resource tree is proposed and verified by experiment which provides a helpful reference to virus detection.


A Novel Similarity Measure for Sequence Data
Mohammad. H. Pandi, Omid Kashefi and Behrouz Minaei
Page: 413~424, Vol. 7, No.3, 2011
10.3745/JIPS.2011.7.3.413
Keywords: Sequence Data, Similarity Measure, Sequence Mining
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A variety of different metrics has been introduced to measure the similarity of two given sequences. These widely used metrics are ranging from spell correctors and categorizers to new sequence mining applications. Different metrics consider different aspects of sequences, but the essence of any sequence is extracted from the ordering of its elements. In this paper, we propose a novel sequence similarity measure that is based on all ordered pairs of one sequence and where a Hasse diagram is built in the other sequence. In contrast with existing approaches, the idea behind the proposed sequence similarity metric is to extract all ordering features to capture sequence properties. We designed a clustering problem to evaluate our sequence similarity metric. Experimental results showed the superiority of our proposed sequence similarity metric in maximizing the purity of clustering compared to metrics such as d2, Smith-Waterman, Levenshtein, and Needleman-Wunsch. The limitation of those methods originates from some neglected sequence features, which are considered in our proposed sequence similarity metric.


Utilizing Various Natural Language Processing Techniques for Biomedical Interaction Extraction
Kyung-Mi Park, Han-Cheol Cho and Hae-Chang Rim
Page: 459~472, Vol. 7, No.3, 2011
10.3745/JIPS.2011.7.3.459
Keywords: Biomedical Interaction Extraction, Natural Language Processing, Interaction Verb Extraction, Argument Relation Identification
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The vast number of biomedical literature is an important source of biomedical interaction information discovery. However, it is complicated to obtain interaction information from them because most of them are not easily readable by machine. In this paper, we present a method for extracting biomedical interaction information assuming that the biomedical Named Entities (NEs) are already identified. The proposed method labels all possible pairs of given biomedical NEs as INTERACTION or NOINTERACTION by using a Maximum Entropy (ME) classifier. The features used for the classifier are obtained by applying various NLP techniques such as POS tagging, base phrase recognition, parsing and predicate-argument recognition. Especially, specific verb predicates (activate, inhibit, diminish and etc.) and their biomedical NE arguments are very useful features for identifying interactive NE pairs. Based on this, we devised a twostep method: 1) an interaction verb extraction step to find biomedically salient verbs, and 2) an argument relation identification step to generate partial predicate-argument structures between extracted interaction verbs and their NE arguments. In the experiments, we analyzed how much each applied NLP technique improves the performance. The proposed method can be completely improved by more than 2% compared to the baseline method. The use of external contextual features, which are obtained from outside of NEs, is crucial for the performance improvement. We also compare the performance of the proposed method against the co-occurrence-based and the rule-based methods. The result demonstrates that the proposed method considerably improves the performance.


An Efficient DVS Algorithm for Pinwheel Task Schedules
Da-Ren Chen and You-Shyang Chen
Page: 613~626, Vol. 7, No.4, 2011
10.3745/JIPS.2011.7.4.613
Keywords: Hard Real-time Systems, Power-aware Scheduling, Dynamic Voltage Scaling, Pinwheel Tasks
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In this paper, we focus on the pinwheel task model with a variable voltage processor with d discrete voltage/speed levels. We propose an intra-task DVS algorithm, which constructs a minimum energy schedule for k tasks in O(d+k log k) time. We also give an inter-task DVS algorithm with O(d+n log n) time, where n denotes th e number of jobs. Previous approaches solve this problem by generating a canonical schedule beforehand and adjusting the tasks’ speed in O(dn log n) or O(n3) time. However, the length of a canonical schedule depends on the hyper period of those task periods and is of exponential length in general. In our approach, the tasks with arbitrary periods are first transformed into harmonic periods and then profile their key features. Afterward, an optimal discrete voltage schedule can be computed directly from those features.


Strategic Information Systems Alignment: Alignment of IS/IT with Business Strategy
Abdisalam Issa-Salwe, Munir Ahmed, Khalid Aloufi and Muhammad Kabir
Page: 121~128, Vol. 6, No.1, 2010
10.3745/JIPS.2010.6.1.121
Keywords: Information Systems, Information Systems, Business Planning, Planning Strategy, IT/IS Alignment.
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Using information systems effectively requires an understanding of the organisation, management, and the technology shaping the systems. All information systems can be described as organisational and management solutions to challenges posed by the environment. The advances in information systems have affect on our day-to day lives . As the technology is evolving immensely so are the opportunities in a healthy way to prepare the organisation in the competitive advantage environment In order to manage the IS/IT based systems, it is important to have an appropriate strategy that defines the systems and provide means to manage them. Strategic Information Systems Alignment (SISA) is an effective way of developing and maintaining the IS/IT systems that support the business operations. Alignment of the IS/IT plans and the business plans is essential for improved business performance, this research looks at the key features of SISA in the changing business circumstances in Saudi Arabia.


A Classifiable Sub-Flow Selection Method for Traffic Classification in Mobile IP Networks
Akihiro Satoh, Toshiaki Osada, Toru Abe, Gen Kitagata, Norio Shiratori and Tetsuo Kinoshita
Page: 307~322, Vol. 6, No.3, 2010
10.3745/JIPS.2010.6.3.307
Keywords: Mobile IP Network, Traffic Classification, Network Management, Traffic Engineering, Machine Learning
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Traffic classification is an essential task for network management. Many researchers have paid attention to initial sub-flow features based classifiers for traffic classification. However, the existing classifiers cannot classify traffic effectively in mobile IP networks. The classifiers depend on initial sub-flows, but they cannot always capture the sub-flows at a point of attachment for a variety of elements because of seamless mobility. Thus the ideal classifier should be capable of traffic classification based on not only initial sub-flows but also various types of sub-flows. In this paper, we propose a classifiable sub-flow selection method to realize the ideal classifier. The experimental results are so far promising for this research direction, even though they are derived from a reduced set of general applications and under relatively simplifying assumptions. Altogether, the significant contribution is indicating the feasibility of the ideal classifier by selecting not only initial sub-flows but also transition sub-flows.


Interface Development for the Point-of-care device based on SOPC
Hong Bum Son, Sung Gun Song, Jae Wook Jung, Chang Su Lee and Seong Mo Park
Page: 16~20, Vol. 3, No.1, 2007
None
Keywords: Point-Of-Care, System-On-a-Programmable-Chip, Interface, Driver, Linux, ?C/OS-II
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This paper describes the development of the sensor interface and driver program for a point of care (POC) device. The proposed POC device comprises an ARM9 embedded processor and eightchannel sensor input to measure various bio-signals. It features a user-friendly interface using a fullcolor TFT-LCD and touch-screen, and a bluetooth wireless communication module. The proposed device is based on the system on a programmable chip (SOPC). We use Altera¡¯s Excalibur device, which has an ARM9 and FPGA area on a chip, as a test bed for the development of interface hardware and driver software.


Feature Extraction of Concepts by Independent Component Analysis
Altangerel Chagnaa, Cheol-Young Ock, Chang-Beom Lee and Purev Jaimai
Page: 33~37, Vol. 3, No.1, 2007
None
Keywords: Independent Component Analysis, Clustering, Latent Concepts.
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Semantic clustering is important to various fields in the modern information society. In this work we applied the Independent Component Analysis method to the extraction of the features of latent concepts. We used verb and object noun information and formulated a concept as a linear combination of verbs. The proposed method is shown to be suitable for our framework and it performs better than a hierarchical clustering in latent semantic space for finding out invisible information from the data.


A Feature Selection Technique based on Distributional Differences
Sung-Dong Kim
Page: 23~27, Vol. 2, No.1, 2006
None
Keywords: Feature Selection, Distributional Differences
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This paper presents a feature selection technique based on distributional differences for efficient machine learning. Initial training data consists of data including many features and a target value. We classified them into positive and negative data based on the target value. We then divided the range of the feature values into 10 intervals and calculated the distribution of the intervals in each positive and negative data. Then, we selected the features and the intervals of the features for which the distributional differences are over a certain threshold. Using the selected intervals and features, we could obtain the reduced training data. In the experiments, we will show that the reduced training data can reduce the training time of the neural network by about 40%, and we can obtain more profit on simulated stock trading using the trained functions as well.


Robust Real-time Intrusion Detection System
Byung-Joo Kim and Il-Kon Kim
Page: 9~13, Vol. 1, No.1, 2005
None
Keywords: real-time IDS, kernel PCA. LS-SVM
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Computer security has become a critical issue with the rapid development of business and other ftansaction systems over the Intemet. The application of atlificial intelligence, machine learning and data mining techdques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performaace of a classifier. Selecting important features from input data leads to simplification olthe problem, and faster and more accuate detection rates. Thus selecting important features is ar impofiant issue in intrusion detection. Alother issue in intrusion detection is that inost of the intrusion detection systems are performed by offJine and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an online feature extraction method with the Least Squares Suppofi Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intntsion detection systems.


A Hierarchical Text Rating System for Objectionable Documents
Chi Yoon Jeong, Seung Wan Han and Taek Yong Nam
Page: 22~26, Vol. 1, No.1, 2005
None
Keywords: Objectionable documents, document analysis, text classification, hierarchical system, SVM
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In this paper, we classified the objectionable texts into four rates according to their harmfulness and proposed the hierarchical text rating system for objectionable documents. Since the documents in the same category have similarities in used words, expressions and structure of the document, the text rating system, which uses a single classification model, has low accuracy. To solve this problem, we separate objectionable documents into several subsets by using their properties, and then classify the subsets hierarchically. The proposed system consists of three layers. In each layer, we select features using the chi-square statistics, and then the weight of the features, which is calculated by using the TF-IDF weighting scheme, is used as an input of the non-linear SVM classifier. By means of a hierarchical scheme using the different features and the different number of features in each layer, we can characterize the objectionability of documents more effectively and expect to improve the performance of the rating system. We compared the performance of the proposed system and performance of several text rating systems and experimental results show that the proposed system can archive an excellent classification performance.