Research on Image Recognition Technology Optimization based on Deep Learning
Accurate recognition of images has very important research significance. Image recognition technology plays an important role in many fields such as medicine, aerospace, military, industry and agriculture. Most of the current image recognition methods use manual extraction features, which is not only time-consuming and laborious, but also difficult to extract. Deep learning is an unsupervised learning. The label value of the sample can be unknown during the learning process. The whole process can be extracted without manual intervention. feature. In recent years, the use of deep learning for image recognition has become a research hotspot in the field of image recognition. It has achieved good results and has a broad research space. Image recognition technology is an important field of artificial intelligence. Traditional image recognition methods require artificial design features, while deep learning belongs to neural network structure. It can automatically learn features from big data, which greatly improves recognition accuracy and efficiency. Therefore, this paper focuses on image recognition methods based on deep learning, and discusses the basic models and principles of convolutional neural networks and deep belief networks.
Deep Learning Image Recognition Optimization and Application
Li Li
Department of Information Engineering, College of Humanities and Information Changchun University of Technology, Changchun, Jilin, 130000, China
国际会议
呼和浩特
英文
114-117
2019-07-27（万方平台首次上网日期，不代表论文的发表时间）