Mixed Dish Recognition through Multi-Label Learning

Mix dish recognition, whose goal is to identify each of the dish type presented on one plate, is generally regarded as a difficult problem. The major challenge of this problem is that different dishes presented in one plate may overlap with each other and there may be no clear boundaries among them. Therefore, labeling the bounding box of each dish type is difficult and not necessarily leading to good results. This paper studies the problem from the perspective of multi-label learning. Specially, we propose to perform dish recognition on region level with multiple granularities. For experimental purpose, we collect two mix dish datasets: mixed economic rice and economic beehoon. The experimental results on these two datasets demonstrate the effectiveness of the proposed region-level multi-label learning methods.