An End-to-End Image-Based Automatic Food Energy Estimation Technique Based on Learned Energy Distribution Images: Protocol and Methodology

• Main Authors: Shaobo Fang, Zeman Shao, Deborah A. Kerr, Carol J. Boushey, Fengqing Zhu
• Format: Article
• Language: English
• Published: MDPI AG 2019-04-01
• Series: Nutrients
• Subjects: dietary assessment; food energy estimation; generative models; generative adversarial networks; image-to-energy mapping; neural networks; regressions
• Online Access: https://www.mdpi.com/2072-6643/11/4/877

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.