In this article, we propose a mobile food recognition system that uses the picture of the food, taken by the user’s mobile device, to recognize multiple food items in the same meal, such as steak and potatoes on the same plate, to estimate the calorie and nutrition of the meal. To speed up and make the process more accurate, the user is asked to quickly identify the general area of the food by drawing a bounding circle on the food picture by touching the screen. The system then uses image processing and computational intelligence for food item recognition. The advantage of recognizing items, instead of the whole meal, is that the system can be trained with only single item food images. At the training stage, we first use region proposal algorithms to generate candidate regions and extract the convolutional neural network (CNN) features of all regions. Second, we perform region mining to select positive regions for each food category using maximum cover by our proposed submodular optimization method. At the testing stage, we first generate a set of candidate regions. For each region, a classification score is computed based on its extracted CNN features and predicted food names of the selected regions. Since fast response is one of the important parameters for the user who wants to eat the meal, certain heavy computational parts of the application are offloaded to the cloud. Hence, the processes of food recognition and calorie estimation are performed in cloud server. Our experiments, conducted with the FooDD dataset, show an average recall rate of 90.98%, precision rate of 93.05%, and accuracy of 94.11% compared to 50.8% to 88% accuracy of other existing food recognition systems.
Calorie measurement systems that run on smart phones allow the user to take a picture of the food and measure the number of calories automatically. In order to identify the food accurately in such systems, image segmentation, which partitions an image into different regions, plays an important role. In this paper, we present the implementation of Graph cut segmentation as a means of improving the accuracy of our food classification and recognition system. Graph cut based method is well-known to be efficient, robust, and capable of finding the best contour of objects in an image, suggesting it to be a good method for separating food portions in a food image for calorie measurement. In this paper, we provide the analysis of the Graph cut algorithm as applied to food recognition. We also perform a number of experiments where we used results from the segmentation phase to the Support Vector Machine (SVM) classification model. The results show an improvement in the accuracy of food recognition, especially mixed food where accuracy increases by 15% compared to our previous work [10].
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