Food items detection and recognition via multiple deep models

We address the problem of food items detection and recognizing different food categories in images. Given the variety of food items with low inter- and high intraclass variations and the limited information contained in a single image, the problem is known to be particularly hard. In order to achieve better detection and recognition capabilities, we propose a joint use of multiple classifiers trained on features extracted via multiple deep models using different fusion techniques, including an early and two different late fusion schemes, namely induced order weighted averaging and particle swarm optimization based fusion. Moreover, we assess the performance of different deep models in food items detection and recognition. Experimental evaluations are carried out on two large-scale benchmark datasets, demonstrating better results for the proposed approach.