Uncertainty Modeling and Deep Learning Applied to Food Image Analysis

Recognizing food images arises as a difficult image recognition task due to the high intra-class variance and low inter-class variance of food categories. Deep learning has been shown as a promising methodology to address such difficult problems as food image recognition that can be considered as a fine-grained object recognition problem. We argue that, in order to continue improving performance in this task, it is necessary to better understand what the model learns instead of considering it as a black box. In this paper, we show how uncertainty analysis can help us gain a better understanding of the model in the context of the food recognition. Furthermore, we take decisions to improve its performance based on this analysis and propose a new data augmentation approach considering sample-level uncertainty. The results of our method considering the evaluation on a public food dataset are very encouraging.