Mobile Multi-Food Recognition Using Deep Learning

Abstract

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.