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A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment

Dietary studies showed that dietary problems such as obesity are associated with other chronic diseases, including hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are ma...

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Main Authors: Tahir, Ghalib Ahmed, Loo, Chu Kiong
Format: Article
Published: MDPI 2021
Subjects:
R Medicine
RA Public aspects of medicine
Online Access:http://eprints.um.edu.my/27548/
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spelling my.um.eprints.275482022-04-04T04:11:16Z http://eprints.um.edu.my/27548/ A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment Tahir, Ghalib Ahmed Loo, Chu Kiong R Medicine RA Public aspects of medicine Dietary studies showed that dietary problems such as obesity are associated with other chronic diseases, including hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable using interactive mHealth apps. However, traditional dietary monitoring systems using manual food logging suffer from imprecision, underreporting, time consumption, and low adherence. Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through machine learning methods. This survey discusses the best-performing methodologies that have been developed so far for automatic food recognition and volume estimation. Firstly, the paper presented the rationale of visual-based methods for food recognition. Then, the core of the study is the presentation, discussion, and evaluation of these methods based on popular food image databases. In this context, this study discusses the mobile applications that are implementing these methods for automatic food logging. Our findings indicate that around 66.7% of surveyed studies use visual features from deep neural networks for food recognition. Similarly, all surveyed studies employed a variant of convolutional neural networks (CNN) for ingredient recognition due to recent research interest. Finally, this survey ends with a discussion of potential applications of food image analysis, existing research gaps, and open issues of this research area. Learning from unlabeled image datasets in an unsupervised manner, catastrophic forgetting during continual learning, and improving model transparency using explainable AI are potential areas of interest for future studies. MDPI 2021-12 Article PeerReviewed Tahir, Ghalib Ahmed and Loo, Chu Kiong (2021) A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment. Healthcare, 9 (12). ISSN 2227-9032, DOI https://doi.org/10.3390/healthcare9121676 <https://doi.org/10.3390/healthcare9121676>. 10.3390/healthcare9121676
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic R Medicine
RA Public aspects of medicine
spellingShingle R Medicine
RA Public aspects of medicine
Tahir, Ghalib Ahmed
Loo, Chu Kiong
A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment
description Dietary studies showed that dietary problems such as obesity are associated with other chronic diseases, including hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable using interactive mHealth apps. However, traditional dietary monitoring systems using manual food logging suffer from imprecision, underreporting, time consumption, and low adherence. Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through machine learning methods. This survey discusses the best-performing methodologies that have been developed so far for automatic food recognition and volume estimation. Firstly, the paper presented the rationale of visual-based methods for food recognition. Then, the core of the study is the presentation, discussion, and evaluation of these methods based on popular food image databases. In this context, this study discusses the mobile applications that are implementing these methods for automatic food logging. Our findings indicate that around 66.7% of surveyed studies use visual features from deep neural networks for food recognition. Similarly, all surveyed studies employed a variant of convolutional neural networks (CNN) for ingredient recognition due to recent research interest. Finally, this survey ends with a discussion of potential applications of food image analysis, existing research gaps, and open issues of this research area. Learning from unlabeled image datasets in an unsupervised manner, catastrophic forgetting during continual learning, and improving model transparency using explainable AI are potential areas of interest for future studies.
format Article
author Tahir, Ghalib Ahmed
Loo, Chu Kiong
author_facet Tahir, Ghalib Ahmed
Loo, Chu Kiong
author_sort Tahir, Ghalib Ahmed
title A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment
title_short A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment
title_full A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment
title_fullStr A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment
title_full_unstemmed A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment
title_sort comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment
publisher MDPI
publishDate 2021
url http://eprints.um.edu.my/27548/
_version_ 1735409517949091840
score 13.235362

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