Learning CNN-based Features for Retrieval of Food Images

Recently a huge amount of work has been done in order to develop Convolutional Neural Networks (CNNs) for supervised food recognition. These CNNs are trained to classify a predefined set of food classes within a specific food dataset. CNN-based features have been largely experimented for many image retrieval domains and to a lesser extent to the food domain. In this paper, we investigate the use of CNN-based features for food retrieval by taking advantage of existing food datasets. To this end, we have built the Food524DB, the largest publicly available food dataset with 524 food classes and 247,636 images by merging food classes from existing datasets in the state of the art. We have then used this dataset to fine tune a Residual Network, ResNet-50, which has demonstrated to be very effective for image recognition. The last fully connected layer is finally used as feature vector for food image indexing and retrieval. Experimental results are reported on the UNICT-FD1200 dataset that has been specifically design for food retrieval.