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 food analysis, once images containing food are identified, is usually the next step to apply. Again, CNN-based models have been able to progressively improve the results of food recognition models reaching an accuracy of about 90 Most of the approaches focused on food recognition only exploit the visual content, but they ignore the context. However, geolocation and other information have also been explored in the literature for restaurant-oriented food recognition: on-line restaurant information is used in @cite_43 , similarly to @cite_22 in which nutritional information is also retrieved; whilst the menu, the location and user images of dished are used in @cite_39 . On the other hand, @cite_2 go a step further since their target is not only to improve both classification performance and efficiency, but also to better model contextual data and its relation with the other elements.
@cite_43 @cite_2 @cite_22 @cite_39
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Food-related photos have become increasingly popular , due to social networks, food recommendations, and dietary assessment systems. Reliable annotation is essential in those systems, but unconstrained automatic food recognition is still not accurate enough. Most works focus on exploiting only the visual content while ignoring the context. To address this limitation, in this paper we explore leveraging geolocation and external information about restaurants to simplify the classification problem. We propose a framework incorporating discriminative classification in geolocalized settings and introduce the concept of geolocalized models, which, in our scenario, are trained locally at each restaurant location. In particular, we propose two strategies to implement this framework: geolocalized voting and combinations of bundled classifiers. Both models show promising performance, and the later is particularly efficient and scalable. We collected a restaurant-oriented food dataset with food images, dish tags, and restaurant-level information, such as the menu and geolocation. Experiments on this dataset show that exploiting geolocation improves around 30 the recognition performance, and geolocalized models contribute with an additional 3–8 absolute gain, while they can be trained up to five times faster. In this paper, we examined the effectiveness of deep convolutional neural network (DCNN) for food photo recognition task. Food recognition is a kind of fine-grained visual recognition which is relatively harder problem than conventional image recognition. To tackle this problem, we sought the best combination of DCNN-related techniques such as pre-training with the large-scale ImageNet data, fine-tuning and activation features extracted from the pre-trained DCNN. From the experiments, we concluded the fine-tuned DCNN which was pre-trained with 2000 categories in the ImageNet including 1000 food-related categories was the best method, which achieved 78.77 as the top-1 accuracy for UEC-FOOD100 and 67.57 for UEC-FOOD256, both of which were the best results so far. In addition, we applied the food classifier employing the best combination of the DCNN techniques to Twitter photo data. We have achieved the great improvements on food photo mining in terms of both the number of food photos and accuracy. In addition to its high classification accuracy, we found that DCNN was very suitable for large-scale image data, since it takes only 0.03 seconds to classify one food photo with GPU. Food photos are widely used in food logs for diet monitoring and in social networks to share social and gastronomic experiences. A large number of these images are taken in restaurants. Dish recognition in general is very challenging, due to different cuisines, cooking styles, and the intrinsic difficulty of modeling food from its visual appearance. However, contextual knowledge can be crucial to improve recognition in such scenario. In particular, geocontext has been widely exploited for outdoor landmark recognition. Similarly, we exploit knowledge about menus and location of restaurants and test images. We first adapt a framework based on discarding unlikely categories located far from the test image. Then, we reformulate the problem using a probabilistic model connecting dishes, restaurants, and locations. We apply that model in three different tasks: dish recognition, restaurant recognition, and location refinement. Experiments on six datasets show that by integrating multiple evidences (visual, location, and external knowledge) our system can boost the performance in all tasks. We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories. The simplest version assumes that the user is eating at a restaurant for which we know the menu. In this case, we can collect images offline to train a multi-label classifier. At run time, we apply the classifier (running on your phone) to predict which foods are present in your meal, and we lookup the corresponding nutritional facts. We apply this method to a new dataset of images from 23 different restaurants, using a CNN-based classifier, significantly outperforming previous work. The more challenging setting works outside of restaurants. In this case, we need to estimate the size of the foods, as well as their labels. This requires solving segmentation and depth volume estimation from a single image. We present CNN-based approaches to these problems, with promising preliminary results.