A Manually Captured and Modified Phone Screen Image Dataset for Widget Classification on CNNs


SungChul Byun, Seong-Soo Han, Chang-Sung Jeong, Journal of Information Processing Systems Vol. 18, No. 2, pp. 197-207, Apr. 2022  

https://doi.org/10.3745/JIPS.02.0169
Keywords: Captured Image, CNN, Deep Learning Dataset, Image Classification, Object Detection, Widget
Fulltext:

Abstract

The applications and user interfaces (UIs) of smart mobile devices are constantly diversifying. For example, deep learning can be an innovative solution to classify widgets in screen images for increasing convenience. To this end, the present research leverages captured images and the ReDraw dataset to write deep learning datasets for image classification purposes. First, as the validation for datasets using ResNet50 and EfficientNet, the experiments show that the dataset composed in this study is helpful for classification according to a widget's functionality. An implementation for widget detection and classification on RetinaNet and EfficientNet is then executed. Finally, the research suggests the Widg-C and Widg-D datasets—a deep learning dataset for identifying the widgets of smart devices—and implementing them for use with representative convolutional neural network models.


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Cite this article
[APA Style]
Byun, S., Han, S., & Jeong, C. (2022). A Manually Captured and Modified Phone Screen Image Dataset for Widget Classification on CNNs. Journal of Information Processing Systems, 18(2), 197-207. DOI: 10.3745/JIPS.02.0169.

[IEEE Style]
S. Byun, S. Han, C. Jeong, "A Manually Captured and Modified Phone Screen Image Dataset for Widget Classification on CNNs," Journal of Information Processing Systems, vol. 18, no. 2, pp. 197-207, 2022. DOI: 10.3745/JIPS.02.0169.

[ACM Style]
SungChul Byun, Seong-Soo Han, and Chang-Sung Jeong. 2022. A Manually Captured and Modified Phone Screen Image Dataset for Widget Classification on CNNs. Journal of Information Processing Systems, 18, 2, (2022), 197-207. DOI: 10.3745/JIPS.02.0169.