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A Method for Improving CNN-Based Image Recognition Using DCGAN

Wei Fang1,2, Feihong Zhang1,*, Victor S. Sheng3, Yewen Ding1
School of Computer & Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
State Key Lab. for Novel Software Technology, Nanjing University, Nanjing, 210023, China.
Computer Science Department, University of Central Arkansas, Conway AR, 72035, USA.
* Corresponding Author: Feihong Zhang. Email: .

Computers, Materials & Continua 2018, 57(1), 167-178. https://doi.org/10.32604/cmc.2018.02356

Abstract

Image recognition has always been a hot research topic in the scientific community and industry. The emergence of convolutional neural networks(CNN) has made this technology turned into research focus on the field of computer vision, especially in image recognition. But it makes the recognition result largely dependent on the number and quality of training samples. Recently, DCGAN has become a frontier method for generating images, sounds, and videos. In this paper, DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating model. We combine DCGAN with CNN for the second time. Use DCGAN to generate samples and training in image recognition model, which based by CNN. This method can enhance the classification model and effectively improve the accuracy of image recognition. In the experiment, we used the radar profile as dataset for 4 categories and achieved satisfactory classification performance. This paper applies image recognition technology to the meteorological field.

Keywords

DCGAN, image recognition, CNN, samples.

Cite This Article

W. Fang, F. Zhang, V. S. Sheng and Y. Ding, "A method for improving cnn-based image recognition using dcgan," Computers, Materials & Continua, vol. 57, no.1, pp. 167–178, 2018.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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