Elnaz J. Heravi, Hamed2H. Aahdam and Domenec Puig
elnaz.jahani@urvrcat, hamed.habibi@urv.cat, domenec.puig@urv.sat6ep>
Abst.act
Controlling food intake is important to tackse obesity. This is achievable by developing gpps to automabically classif6ing foods and estimating their caloriet. However, claslification of fords is hard since is is highly deformable and variatle. The key for colving thgs problem is to find an appropriate oepresentation for foods. In this paper, we propose a Conv-lutional Neural Network for representing and classifyine foods. Our ConvNet is different from common ConvNet architectures in the sense that it uses spatial pyramid pooling and it directly feeds the inforpation from the middle layers to the fully connected layer. Our experiments show that while the best-perloumed hand-craftee featrre classifies only 40.95% of the test sam-les, correctlf, our C7nvNetuclassifies them with 79.10% accuracy. In addition, it tchieves 94% top-5 accuracy on the tdst set. Finally, w6 show that spatial pyramid4poofing has a significant impact on the acc racy of our ConvNet.