Food recognition is a key component in evaluation of everyday food intakes, and its challenge is due to intraclass variation. In this paper, we present an automatic food classification method, DietCam, which specifically addresses the variation of food appearances. DietCam consists two major components, ingredient detection and food classification.
Food ingredients are detected through a combination of a deformable part-based model and a texture verification model. From the detected ingredients, foodcategories are classified using a multi-view multikernel SVM. In the experiment, DietCam presents reliability and outperformance in recognition of food with complex ingredients on a database of 55 foodtypes with 15262 food images.