Where and What to Eat: Simultaneous Restaurant and Dish Recognition from Food Image

This paper considers the problem of simultaneous restaurant and dish recognition from food images. Since the restaurants are known because of their some special dishes (e.g., the dish “hamburger” in the restaurant “KFC” ), the dish semantics from the food image provides partial evidence for the restaurant identity. Therefore, instead of exploiting the binary correlation between food images and dish labels by existing work, we model food images, their dish names and restaurant information jointly, which is expected to enable novel applications, such as food image based restaurant visualization and recommendation. For solution, we propose a model, namely Partially Asymmetric Multi-Task Convolutional Neural Network (PAMT-CNN), which includes the dish pathway and the restaurant pathway to learn the dish semantics and the restaurant identity, respectively. Considering the dependence of the restaurant identity on the dish semantics, PAMT-CNN is capable of learning the restaurant’s identity under the guidance of the dish pathway using partially asymmetric shared network architecture. To evaluate our model, we construct one food image dataset with 24,690 food images, 100 classes of restaurants and 100 classes of dishes. The evaluation results on this dataset have validated the effectiveness of the proposed approach.