A food image recognition system with multiple kernel learning

Authors:
Taichi Joutou;Keiji Yanai
Affiliations:
Department of Computer Science, The University of Electro-Communications, Chofu-shi, Tokyo, Japan;Department of Computer Science, The University of Electro-Communications, Chofu-shi, Tokyo, Japan
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

Since health care on foods is drawing people's attention recently, a system that can record everyday meals easily is being awaited. In this paper, we propose an automatic food image recognition system for recording people's eating habits. In the proposed system, we use the Multiple Kernel Learning (MKL) method to integrate several kinds of image features such as color, texture and SIFT adaptively. MKL enables to estimate optimal weights to combine image features for each category. In addition, we implemented a prototype system to recognize food images taken by cellular-phone cameras. In the experiment, we have achieved the 61.34% classification rate for 50 kinds of foods. To the best of our knowledge, this is the first report of a food image classification system which can be applied for practical use.