8 December 2015 A deep convolutional neural network for recognizing foods
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Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 98751D (2015) https://doi.org/10.1117/12.2228875
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain
Abstract
Controlling the food intake is an efficient way that each person can undertake to tackle the obesity problem in countries worldwide. This is achievable by developing a smartphone application that is able to recognize foods and compute their calories. State-of-art methods are chiefly based on hand-crafted feature extraction methods such as HOG and Gabor. Recent advances in large-scale object recognition datasets such as ImageNet have revealed that deep Convolutional Neural Networks (CNN) possess more representation power than the hand-crafted features. The main challenge with CNNs is to find the appropriate architecture for each problem. In this paper, we propose a deep CNN which consists of 769; 988 parameters. Our experiments show that the proposed CNN outperforms the state-of-art methods and improves the best result of traditional methods 17%. Moreover, using an ensemble of two CNNs that have been trained two different times, we are able to improve the classification performance 21:5%.
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Elnaz Jahani Heravi, Hamed Habibi Aghdam, Domenec Puig, "A deep convolutional neural network for recognizing foods", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98751D (8 December 2015); doi: 10.1117/12.2228875; https://doi.org/10.1117/12.2228875
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