DietCam: Multiview Food Recognition Using a Multikernel SVM

Abstract— 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 of 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, food categories are classified using a multiview multikernel SVM. In the experiment, DietCam presents reliability and outperformance in recognition of food with complex ingredients on a database including 15,262 food images of 55 food types. Object recognition has been one of the fundamental areas in pattern recognition for decades, producing prosperous results in specific object recognition, such as faces [4] and cars [5], [6]. Food recognition is challenging as compared to specific object recognition because it is essentially an intraclass recognition problem. Intraclass recognition is still unsolved, especially for objects with extreme variation [7], such as animals, furniture, flowers, and food. The appearance of food exhibits a higher degree of variance even for the same food type. < final year projects >