Food recognition is an emerging computer vision topic. The problem is characterized by the absence of rigid struc-ture of the food and by the large intra-class variations. Ex-isting approaches tackle the problem by designing ad-hoc feature representations based on a priori knowledge of the problem. Differently from these, we propose a committee-based recognition system that chooses the optimal features out of the existing plethora of available ones (e.g., color, texture, etc.). Each committee member is an Extreme Learn-ing Machine trained to classify food plates on the basis of a single feature type. Single member classifications are then considered by a structural Support Vector Machine to produce the final ranking of possible matches. This is achieved by filtering out the irrelevant features/classifiers, thus considering only the relevant ones. Experimental re-sults show that the proposed system outperforms state-of-the-art works on the most used three publicly available benchmark datasets.
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