Key points are not available for this paper at this time.
Recognition of food images is challenging due to their diversity and practical for health care on foods for people. In this paper, we propose an automatic food image recognition system for 85 food categories by fusing various kinds of image features including bag-of-features (BoF), color histogram, Gabor features and gradient histogram with Multiple Kernel Learning (MKL). In addition, we implemented a prototype system to recognize food images taken by cellular-phone cameras. In the experiment, we have achieved the 62.52% classification rate for 85 food categories.
Hoashi et al. (Wed,) studied this question.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: