A Simplified CNNs Visual Perception Learning Network Algorithm for Foods Recognition. (June 2021)

• Record Type: Journal Article
• Authors: Xiao, Limei; Lan, Tian; Xu, Dayou; Gao, Weizhe; Li, Ce
• Is Part Of: Computers & electrical engineering. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Food image -- Recognition -- CNNs -- Jumping convolution; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107152
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: With improvements in human living standard, people's demands on food quality are getting higher and higher. Effective food recognition algorithms are needed to obtain more useful food information. In order to solve the problem of low accuracy and slow speed of food recognition algorithms, a new food recognition algorithm based on CNN algorithm is proposed. First, the proposed algorithm preprocess the food images which are collected from the internet. And then use the traditional convolution extract the features from food images. The jumping convolution which is designed in this paper to extract food features jumping and combines the features from traditional convolutions. This algorithm cannot only solve the food recognition problem effectively, but also reduce the calculation parameters. Compared with the experimental results of other deep learning networks, the proposed algorithm has a good effect, and can recognize the food quickly and reduce the training time.