An End-to-End Image-Based Automatic Food Energy Estimation Technique Based on Learned Energy Distribution Images: Protocol and Methodology
An End-to-End Image-Based Automatic Food Energy Estimation Technique Based on Learned Energy Distribution Images: Protocol and Methodology
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DOI：
10.3390/nu11040877
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发表时间：
2019-04-01
期刊：
影响因子：
5.9
通讯作者：
Zhu, Fengqing
中科院分区：
文献类型：
--
作者：
Fang, Shaobo;Shao, Zeman;Zhu, Fengqing
自动获得准确的食物部分估计是具有挑战性的，因为食物制备和消费的过程对食物形状和外观施加了很大的变化。本文的目的是估计食物能量数值从饮食场合使用移动的食物记录捕获的图像。为了模拟进食场景中食物能量分布的特点，引入了食物能量分布的新概念。食物图像到其能量分布的映射是使用生成对抗网络（GAN）架构学习的。根据遗传算法预测的能量分布图像，从图像中估计食物能量。所提出的方法进行了验证，从一个为期7天的饮食研究中收集的一组食物图像45个社区居住的男性和女性之间的21-65岁。从提供给参与者的预先称重的食物中获得地面真实食物能量。将使用我们的端到端能量估计系统预测的食物能量值与地面真实食物能量值进行比较。估计能量的平均误差为每次进食209千卡。这些结果表明，提高基于图像的饮食评估的准确性的承诺。
Obtaining accurate food portion estimation automatically is challenging since the processes of food preparation and consumption impose large variations on food shapes and appearances. The aim of this paper was to estimate the food energy numeric value from eating occasion images captured using the mobile food record. To model the characteristics of food energy distribution in an eating scene, a new concept of food energy distribution was introduced. The mapping of a food image to its energy distribution was learned using Generative Adversarial Network (GAN) architecture. Food energy was estimated from the image based on the energy distribution image predicted by GAN. The proposed method was validated on a set of food images collected from a 7-day dietary study among 45 community-dwelling men and women between 21-65 years. The ground truth food energy was obtained from pre-weighed foods provided to the participants. The predicted food energy values using our end-to-end energy estimation system was compared to the ground truth food energy values. The average error in the estimated energy was 209 kcal per eating occasion. These results show promise for improving accuracy of image-based dietary assessment.