Food Detection and Recognition Using Convolutional Neural Network
- 3 November 2014
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
Abstract
In this paper, we apply a convolutional neural network (CNN) to the tasks of detecting and recognizing food images. Because of the wide diversity of types of food, image recognition of food items is generally very difficult. However, deep learning has been shown recently to be a very powerful image recognition technique, and CNN is a state-of-the-art approach to deep learning. We applied CNN to the tasks of food detection and recognition through parameter optimization. We constructed a dataset of the most frequent food items in a publicly available food-logging system, and used it to evaluate recognition performance. CNN showed significantly higher accuracy than did traditional support-vector-machine-based methods with handcrafted features. In addition, we found that the convolution kernels show that color dominates the feature extraction process. For food image detection, CNN also showed significantly higher accuracy than a conventional method did.Keywords
Funding Information
- Japan Society for the Promotion of Science (Grant-in-Aid for Scientific Research)
- Core Research for Evolutional Science and Technology, Japan Science and Technology Agency
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