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A New Large-scale Food Image Segmentation Dataset and Its Application to Food Calorie Estimation Based on Grains of Rice

To estimate food calorie accurately from food images, accurate food image segmentation is needed. So far no large-scale food image segmentation datasets which have pixel-wise labels exists. In this paper, we added segmentation masks to the food images in the existing dataset, UEC-Food100, semi-automatically. To estimate segmentation masks, we revised bounding boxes included in the UEC-Food100 dataset so that bounding boxes bounds not dish regions but only food regions. As results, by applying GrubCut, we obtained good segmentation masks, and we checked and corrected 1000 images of them if needed by hand for the testing masks. We trained segmentation networks with the newly-created food image masks. As results, segmentation accuracy was much improved, which is expected to bring more accurate food calorie estimation. In addition, we propose a new method on food calorie estimation using grains of steamed rice which are typically contained in Japanese foods instead of a reference card. By the experiments, we show real food size can be estimated from rice images, which helps accurate food calorie estimation.