The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90 in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments.
@cite_29: In this paper, we propose a novel effective framework to expand an existing image dataset automatically leveraging existing categories and crowdsourcing. Especially, in this paper, we focus on expansion on food image data set. The number of food categories is uncountable, since foods are different from a place to a place. If we have a Japanese food dataset, it does not help build a French food recognition system directly. That is why food data sets for different food cultures have been built independently so far. Then, in this paper, we propose to leverage existing knowledge on food of other cultures by a generic “foodness” classifier and domain adaptation. This can enable us not only to built other-cultured food datasets based on an original food image dataset automatically, but also to save as much crowd-sourcing costs as possible. In the experiments, we show the effectiveness of the proposed method over the baselines.
In this paper, we deal with the identification of different foods placed on a food tray, by integrating the four food analysis problems mentioned above. To the best of our knowledge, only one approach with this purpose has been evidenced in the literature @cite_29 . The authors there introduced an additional food dataset composed by images taken in a canteen environment named UNIMIB2016. In addition, they proposed a pipeline for food recognition that performs classification based on the candidate regions obtained by combining two separate images segmentation processes, through saturation and color texture (JSEG). The best result was achieved by combining global and local (path-based) approaches using an SVM as classifier. However, the proposed approach performs the segmentation based on generic methods rather than learning the best discriminant features between different foods based on the dataset. Furthermore, it requires several sequential steps to first segment and then classify, which implies a high processing time. Instead, our method is able to perform the food segmentation and detection processes in parallel, allowing to speed up the processing time.