Computer vision-based food calorie estimation: dataset, method, and experiment
This addresses the need for more accurate calorie estimation in nutrition and health applications, though it is incremental as it builds on existing computer vision methods with new data.
The authors tackled the problem of incomplete calorie estimation from food images by creating a novel dataset with volume and mass records, and they developed a deep learning method using Faster R-CNN for food detection, showing it is effective for calorie estimation with a dataset of 2978 images.
Computer vision has been introduced to estimate calories from food images. But current food image data sets don't contain volume and mass records of foods, which leads to an incomplete calorie estimation. In this paper, we present a novel food image data set with volume and mass records of foods, and a deep learning method for food detection, to make a complete calorie estimation. Our data set includes 2978 images, and every image contains corresponding each food's annotation, volume and mass records, as well as a certain calibration reference. To estimate calorie of food in the proposed data set, a deep learning method using Faster R-CNN first is put forward to detect the food. And the experiment results show our method is effective to estimate calories and our data set contains adequate information for calorie estimation. Our data set is the first released food image data set which can be used to evaluate computer vision-based calorie estimation methods.