Highly Accurate Food/Non-Food Image Classification Based on a Deep Convolutional Neural Network.

Food is an emerging topic of interest for multimedia and computer vision community. In this paper, we investigate food/non-food classification of images. We show that CNN, which is the state of the art technique for general object classification, can perform accurately for this problem. For the experiments, we used three different datasets of images: (1) images we collected from Instagram, (2) Food-101 and Caltech-256 dataset (3) dataset we used in [4]. We investigated the combinations of training and testing using the all three of them. As a result, we achieved high accuracy 96, 95 and 99% in the three datasets respectively.