Deep Learning-Based Food Calorie Estimation Method in Dietary Assessment
This work addresses dietary assessment for obese patients by providing a computer vision-based calorie estimation tool, but it appears incremental as it combines existing methods without major innovations.
The paper tackles the problem of estimating food calories from images to aid obesity treatment, using Faster R-CNN for detection and GrabCut for contour extraction to improve volume estimation accuracy, with experimental results showing the method is effective.
Obesity treatment requires obese patients to record all food intakes per day. Computer vision has been introduced to estimate calories from food images. In order to increase accuracy of detection and reduce the error of volume estimation in food calorie estimation, we present our calorie estimation method in this paper. To estimate calorie of food, a top view and side view is needed. Faster R-CNN is used to detect the food and calibration object. GrabCut algorithm is used to get each food's contour. Then the volume is estimated with the food and corresponding object. Finally we estimate each food's calorie. And the experiment results show our estimation method is effective.