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_31: In this paper, we apply a convolutional neural network (CNN) to the tasks of detecting and recognizing food images. Because of the wide diversity of types of food, image recognition of food items is generally very difficult. However, deep learning has been shown recently to be a very powerful image recognition technique, and CNN is a state-of-the-art approach to deep learning. We applied CNN to the tasks of food detection and recognition through parameter optimization. We constructed a dataset of the most frequent food items in a publicly available food-logging system, and used it to evaluate recognition performance. CNN showed significantly higher accuracy than did traditional support-vector-machine-based methods with handcrafted features. In addition, we found that the convolution kernels show that color dominates the feature extraction process. For food image detection, CNN also showed significantly higher accuracy than a conventional method did.
@cite_18: With the increase of the number of food images on the Internet, we have been developing a food-logging system which has an automated analysis function as a Web application. It can distinguish food images from other images, analyze the food balance, and visualize the log. In this paper, we demonstrate how the performance can be improved by the personalized models. Because our Web application has an interface to review and correct the food analysis results, the generation of the personalized models can be done on-line. Experimental results using two hundred images showed that the extracted image feature vectors differ from user to user but on the other hand the feature vectors and the food balance of each user have a strong correlation. Therefore, the accuracy of the food balance estimation was improved from 37 to 42 on average by the personalized classifier.
The most basic aspect tackled in the literature is the problem that determines the presence or absence of food in an image. This problem is also called food non-food classification or food detection @cite_31 . The first approximation was proposed by @cite_18 , who through the combination of a BoF model and an SVM achieve a high accuracy on a tiny dataset of 600 images. An improvement of about 4