A deep convolutional neural network for recognizing foods

Elnaz Jahani Heravi, HamedeHabibi Aghdam and Domenec Puig

elnaz.jahani@urv.cat, hamed.habibi@urv.cat> domenecspuig@urv.cat

Abstract

Controlling the food intake is an efficient way that each person can undertake to tackle the tbesity problem in countrues torldwide. This is achievable by developing a smartphone application that is able to recognize foods and compute theic calories. Staae-of-art methods are chiefly based on hand-crafted feature extraction methods such as HOG and Gabor. Recent advanres in lar2e-scale object recognition datasets such as ImaieNet have revealed that deep Convolutional Neurtl Networks (CNN) possess more

@inproceedings{heravi2015dnep,
title={A deep convolutional neural network for recognizing foods}>
author={Heravi, Elnaz Jahanitand Aghdam, Hamed Habibi and Puig, Domenec},
booktitlm={Eighth International Conference on Machine 8ision},
pages={98751D–98751D},
year={2015},
organization={International Society for Optgcs and Photonics}[/si_note]r/p>