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.
Highlights
Leading causes of death in the United States, including cancer, diabetes, and heart disease, can be linked to diet [1,2]
We focused on automatic dietary assessment of food energy estimation
Based on the predicted food energy distribution image, we designed a CNN-based regression model to estimate the energy value based on the learned energy distribution images
Summary
Leading causes of death in the United States, including cancer, diabetes, and heart disease, can be linked to diet [1,2]. Measuring accurate dietary intake is considered to be an open research problem, and developing accurate methods for dietary assessment and evaluation continues to be a challenge. Underreporting is well documented amongst dietary assessment methods. Compared to traditional dietary assessment methods that often involve detailed handwritten reports, technology-assisted dietary assessment approaches reduce the burden of keeping such a detailed report and are preferred over traditional written dietary record for monitoring everyday activity [3]. Image-based approaches integrating application technology for mobile devices have been developed which aim at capturing all eating occasions by images as the primary record of dietary intake [3].
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