文献类型：期刊文献

英文题名：Computer vision-based food calorie estimation: Dataset, method, and experiment

作者：Liang, Yanchao[1]; Li, Jianhua[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, China

年份：2017

外文期刊名：arXiv

收录：EI(收录号：20200024415)

语种：英文

外文关键词：Calibration - Computer vision - Deep learning

摘要：Computer vision has been introduced to estimate calories from food images. But current food image datasets dont contain volume and mass records of foods, which leads to an incomplete calorie estimation. In this paper, we present a novel food image dataset with volume and mass records of foods, and a deep learning method for food detection, to make a complete calorie estimation. Our dataset includes 2978 images, and every image contains corresponding each foods annotation, volume and mass records, as well as a certain calibration reference. To estimate calorie of food in the proposed dataset, In this paper, we present a novel food image dataset with volume and mass records of foods. To estimate calorie of food in the proposed dataset, a deep learning method using Faster R-CNN is used to detect the food and calibration object; GrabCut algorithm is used to get each food’s contour. Then we estimate each food’s volume and calorie. The experiment results show our estimation method is effective. Our dataset is the first released food image dataset, which can be used to evaluate computer vision-based calorie estimation methods. Copyright ? 2017, The Authors. All rights reserved.