On filter banks of texture features for mobile food classification

Nowadays obesity has become one of the most common diseases in many countries. To face it, obese people should constantly monitor their daily meals both for self-limitation and to provide useful statistics for their dietitians. This has led to the recent rise in popularity of food diary applications on mobile devices, where the users can manually annotate their food intake. To overcome the tediousness of such a process, several works on automatic image food recognition have been proposed, typically based on texture features extraction and classification. In this work, we analyze different texture filter banks to evaluate their performances and propose a method to automatically aggregate the best features for food classification purposes. Particular emphasis is put in the computational burden of the system to match the limited capabilities of mobile devices.