Food Recognition Using Consensus Vocabularies

Food recognition is an interesting and challenging problem with applications in medical, social and anthropological research areas. The high variability of food images makes the recognition task difficult for current state-of-the-art methods. It has been proved that the exploitation of multiple features to capture complementary aspects of the image contents is useful to improve the discrimination of different food items. In this paper we exploit an image representation based on the consensus among visual vocabularies built on different feature spaces. Starting from a set of visual codebooks, a consensus clustering technique is used to build a consensus vocabulary used to represent food pictures with a Bag-of-Visual-Words paradigm. This new representation is employed together with a SVM for recognition purpose.