CNN-based features for retrieval and classification of food images

Features learned by deep Convolutional Neural Networks (CNNs) have been recognized to be more robust and expressive than hand-crafted ones. They have been successfully used in different computer vision tasks such as object detection, pattern recognition and image understanding. Given a CNN architecture and a training procedure, the efficacy of the learned features depends on the domain-representativeness of the training examples. In this paper we investigate the use of CNN-based features for the purpose of food recognition and retrieval. To this end, we first introduce the Food-475 database, that is the largest publicly available food database with 475 food classes and 247,636 images obtained by merging four publicly available food databases. We then define the food-domain representativeness of different food databases in terms of the total number of images, number of classes of the domain and number of examples for class. Different features are then extracted from a CNN based on the Residual Network with 50 layers architecture and trained on food databases with diverse food-domain representativeness. We evaluate these features for the tasks of food classification and retrieval. Results demonstrate that the features extracted from the Food-475 database outperform the other ones showing that we need larger food databases in order to tackle the challenges in food recognition, and that the created database is a step forward toward this end.