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Proceedings Paper

A deep convolutional neural network for recognizing foods
Author(s): Elnaz Jahani Heravi; Hamed Habibi Aghdam; Domenec Puig
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Paper 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%.

Paper Details

Date Published: 8 December 2015
PDF: 5 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98751D (8 December 2015); doi: 10.1117/12.2228875
Show Author Affiliations
Elnaz Jahani Heravi, Univ. Rovira i Virgili (Spain)
Hamed Habibi Aghdam, Univ. Rovira i Virgili (Spain)
Domenec Puig, Univ. Rovira i Virgili (Spain)


Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

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