A Simplified CNNs Visual Perception Learning Network Algorithm for Foods Recognition

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...

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Bibliographic Details
Published inComputers & electrical engineering Vol. 92; p. 107152
Main Authors Xiao, Limei, Lan, Tian, Xu, Dayou, Gao, Weizhe, Li, Ce
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier Ltd 01.06.2021
Elsevier BV
Subjects
Algorithms
CNNs
Convolution
Deep learning
Feature extraction
Food
Food image
Jumping convolution
Machine learning
Recognition
Visual perception
Visual perception driven algorithms
Food image
Jumping convolution
Recognition
CNNs
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Summary: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.
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2021.107152