Applying deep learning for food image analysis

With the increasing availability of data on the internet, deep learning techniques have been on the rise these past decade. Food images specifically are one of the most commonly shared types of image on social media. Because of this, the problem of food image analysis has been receiving increasing attention these past few years. However, it presents a series of challenges compared to other computer vision problems, which has limited the progress on the field. Nevertheless, some specific methods who capitalize on these challenges have been able to obtain good results. In this thesis, the method of multi-scale multi-view feature aggregation (MSMVFA) applied to food recognition is explored. It is a strategy that has been able to obtain state-of-the-art performances on the literature recently. It capitalizes on merging information from different scales as well as different types, for instance ingredient and dish features. By using data coming from different granularity levels a more robust and discriminative classification is possible. A detailed validation is provided to test the results of the method under different conditions. We see that both multi-scale and multi-view aspects of the strategy can be beneficial for the classification in various conditions for the food recognition problem. i