Example of ultra-fine grained food recognition.
Given three ultra-fine grained classes (middle), Mapo Tofu of Restaurant A, Salt Pepper Tofu of Restaurant B, and Mapo Tofu of Restaurant B. Their relationships can be modeled through three bipartite graphs, fine-grained classes vs. general food dishes (left) and fine-grained classes vs. two ingredients (right). We incorporate the rich bipartite-graph labels (BGL) into convolutional neural network training to improve recognition accuracy.
Given a food image, can a fine-grained object recognition engine tell which restaurant which dish the food belongs to? Such ultra-fine grained image recognition is the key for many applications like search by images, but it is very challenging because it needs to discern subtle difference between classes while dealing with the scarcity of training data. Fortunately, the ultra-fine granularity naturally brings rich relationships among object classes. This work proposes a novel approach to exploit the rich relationships through bipartite-graph labels (BGL). We show how to model BGL in an overall convolutional neural networks and the resulting system can be optimized through back-propagation. We also show that it is computationally efficient in inference thanks to the bipartite structure. To facilitate the study, we construct a new food benchmark dataset, which consists of 37,885 food images collected from 6 restaurants and totally 975 menus. Experimental results on this new food and three other datasets demonstrates BGL advances previous works in fine-grained object recognition.
Available at here.