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

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).


Review on Digital Image Watermarking Based on Singular Value Decomposition
Chengyou Wang, Yunpeng Zhang and Xiao Zhou
Page: 1585~1601, Vol. 13, No.6, 2017
10.3745/JIPS.03.0086
Keywords: Copyright Protection, Tamper Detection, Digital Image Watermarking, Evaluation Indexes, Singular Value Decomposition (SVD)
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Channel Access, Channel Planning, Coexistence Mitigation, IoT, Smart Medical System


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.


Discrete Wavelet Transform and a Singular Value Decomposition Technique for Watermarking Based on an Adaptive Fuzzy Inference System
Salima Lalani and D. D. Doye
Page: 340~347, Vol. 13, No.2, 2017
10.3745/JIPS.03.0067
Keywords: DWT, Fuzzy, SVD, Watermarking
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A watermark is a signal added to the original signal in order to preserve the copyright of the owner of the digital content. The basic challenge for designing a watermarking system is a dilemma between transparency and robustness. If we want a higher rate of transparency, there has to be a compromise in terms of its robustness and vice versa. Also, until now, watermarking is generalized, resulting in the need for a specialized algorithm to work for a specialized image processing application domain. Our proposed technique takes into consideration the image characteristics for watermark insertion and it optimizes transparency and robustness. It achieved a 99.98% retrieval efficiency for an image blurring attack and counterfeits other attacks. Our proposed technique counterfeits almost all of the image processing attacks.


Content Based Dynamic Texture Analysis and Synthesis Based on SPIHT with GPU
Premanand P Ghadekar and Nilkanth B Chopade
Page: 46~56, Vol. 12, No.1, 2016
10.3745/JIPS.02.0009
Keywords: Discrete Wavelet Transform, Dynamic Texture, GPU, SPIHT, SVD
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Dynamic textures are videos that exhibit a stationary property with respect to time (i.e., they have patterns that repeat themselves over a large number of frames). These patterns can easily be tracked by a linear dynamic system. In this paper, a model t...


A Novel DWT-SVD Canny-Based Watermarking Using a Modified Torus Technique
Salima Lalani and D. D. Doye
Page: 681~687, Vol. 12, No.4, 2016
10.3745/JIPS.02.0045
Keywords: DWT, Image Watermarking, Torus
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Today’s modern world requires a digital watermarking technique that takes the redundancy of an image into consideration for embedding a watermark. The novel algorithm used in this paper takes into consideration the redundancies of spatial domain and wavelet domain for embedding a watermark. Also, the cryptographybased secret key makes the algorithm difficult to hack and help protect ownership. Watermarking is blind, as it does not require the original image. Few coefficient matrices and secret keys are essential to retrieve the original watermark, which makes it redundant to various intentional attacks. The proposed technique resolves the challenge of optimizing transparency and robustness using a Canny-based edge detector technique. Improvements in the transparency of the cover image can be seen in the computed PSNR value, which is 44.20 dB


Robust and Reversible Image Watermarking Scheme Using Combined DCT-DWT-SVD Transforms
Souad Bekkouch and Kamel Mohamed Faraoun
Page: 406~420, Vol. 11, No.3, 2015
10.3745/JIPS.02.0021
Keywords: Image Security, Image Watermarking, Reversible DWT-DCT-SVD Transform
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We present a secure and robust image watermarking scheme that uses combined reversible DWT-DCT-SVD transformations to increase integrity, authentication, and confidentiality. The proposed scheme uses two different kinds of watermarking images: a reversible watermark, W1, which is used for verification (ensuring integrity and authentication aspects); and a second one, W2, which is defined by a logo image that provides confidentiality. Our proposed scheme is shown to be robust, while its performances are evaluated with respect to the peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), normalized cross-correlation (NCC), and running time. The robustness of the scheme is also evaluated against different attacks, including a compression attack and Salt & Pepper attack.


SVD-LDA: A Combined Model for Text Classification
Nguyen Cao Truong Hai, Kyung-Im Kim and Hyuk-Ro Park
Page: 5~10, Vol. 5, No.1, 2009
10.3745/JIPS.2009.5.1.005
Keywords: Latent Dirichlet Allocation, Singular Value Decomposition, Input Filtering, Text Classification, Data Preprocessing.
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Text data has always accounted for a major portion of the world¡¯s information. As the volume of information increases exponentially, the portion of text data also increases significantly. Text classification is therefore still an important area of research. LDA is an updated, probabilistic model which has been used in many applications in many other fields. As regards text data, LDA also has many applications, which has been applied various enhancements. However, it seems that no applications take care of the input for LDA. In this paper, we suggest a way to map the input space to a reduced space, which may avoid the unreliability, ambiguity and redundancy of individual terms as descriptors. The purpose of this paper is to show that LDA can be perfectly performed in a ¡°clean and clear¡± space. Experiments are conducted on 20 News Groups data sets. The results show that the proposed method can boost the classification results when the appropriate choice of rank of the reduced space is determined.