A Method for Improving CNN-Based Image Recognition Using DCGAN

• 作者: Feihong Zhang Victor S.Sheng Wei Fang Yewen Ding
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: DCGAN IMAGE RECOGNITION CNN SAMPLES
• 年，卷（期）: 2018,（10）
• 页码范围: 167-178
• 页数: 12页
• 分类号: TP3

摘要

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.