Recognition of Chinese food using convolutional neural network

Food recognition is the first step for dietary assessment. Computer vision technology is being viewed as an effective tool for automatic food recognition for monitoring nutrition intake. Of the many food recognition algorithms in the literature, Bag-of-Features model is a proven approach that has shown impressive recognition accuracy. In this paper, we propose a small and efficient convolutional neural network architecture for Chinese food recognition, which is more applicable for resources limited platforms. Our network architecture is designed to model and perform a pipeline of processing similar to the Bag-of-Features approach. The main advantage of the proposed architecture, like other convolutional neural networks, is its ability to unifiedly optimize the entire network through back propagation, which is critical to recognition accuracy. We further compare and correlate our architecture with the traditional Bag-of-Features model in an attempt to investigate the similarities between them and identify factors that influence the recognition accuracy. The proposed architecture with a 5-layer deep convolutional neural network achieves the top-1 accuracy of 97.12% and the top-5 accuracy of 99.86% on a newly created Chinese food image dataset that is composed of 8734 images of 25 food categories. Our experimental result demonstrates the feasibility of applying the proposed compact CNN architecture to a challenging problem and achieve real-time performance.