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

1
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
2
School of Public Health, Curtin University, Perth, WA 6845, Australia
3
Curtin Institute of Computation, Curtin University, Perth, WA 6845, Australia
4
Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
5
Department of Nutrition, Purdue University, West Lafayette, IN 47907, USA
*
Author to whom correspondence should be addressed.
Nutrients 2019, 11(4), 877; https://doi.org/10.3390/nu11040877
Received: 8 March 2019 / Revised: 5 April 2019 / Accepted: 12 April 2019 / Published: 18 April 2019
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Abstract

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. View Full-Text
Keywords: dietary assessment; food energy estimation; generative models; generative adversarial networks; image-to-energy mapping; neural networks; regressions dietary assessment; food energy estimation; generative models; generative adversarial networks; image-to-energy mapping; neural networks; regressions
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Fang, S.; Shao, Z.; Kerr, D.A.; Boushey, C.J.; Zhu, F. An End-to-End Image-Based Automatic Food Energy Estimation Technique Based on Learned Energy Distribution Images: Protocol and Methodology. Nutrients 2019, 11, 877.

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