Description

State recognition of food images is a recent topic that is gaining a huge interest in the Computer Vision community. Recently, researchers presented a dataset of food images at different states where unfortunately no information regarding the food category was included. On the overall, it seems that the state recognition problem is more approachable by the CNN-based features than the food classification one. Automated food detection and recognition methods have been studied to enhance end-user life. However, most existing research focused on food ingredient type recognition, with little work has been done for food ingredient state recognition. Successful recognition of food ingredient state plays a significant role in handling the food ingredient by an intelligent system. This could be associated with the visual appearance of the state classes where the texture is more important in the discrimination of the different states. Finally, we test the generalization capability of the most performing deep features dataset of food states. This last experiment shows that the features extracted from a CNN (VGG-19) and Resnet trained on our proposed dataset achieve performance quite close to the one achieved by the state of the art method.