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.
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.
@cite_29
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In this paper, we propose a two-step method to recognize multiple-food images by detecting candidate regions with several methods and classifying them with various kinds of features. In the first step, we detect several candidate regions by fusing outputs of several region detectors including Felzenszwalb's deformable part model (DPM) [1], a circle detector and the JSEG region segmentation. In the second step, we apply a feature-fusion-based food recognition method for bounding boxes of the candidate regions with various kinds of visual features including bag-of-features of SIFT and CSIFT with spatial pyramid (SP-BoF), histogram of oriented gradient (HoG), and Gabor texture features. In the experiments, we estimated ten food candidates for multiple-food images in the descending order of the confidence scores. As results, we have achieved the 55.8 classification rate, which improved the baseline result in case of using only DPM by 14.3 points, for a multiple-food image data set. This demonstrates that the proposed two-step method is effective for recognition of multiple-food images.