A review on vision-based analysis for automatic dietary assessment

doi:10.1016/j.tifs.2022.02.017

	A review on vision-based analysis for automatic dietary assessment
	Wang, Wei 1,3,4; Min, Weiqing 2,5; Li, Tianhao 2,5; Dong, Xiaoxiao 1,3,4; Li, Haisheng 1,3,4; Jiang, Shuqiang 2,5
	2022-04-01
发表期刊	TRENDS IN FOOD SCIENCE & TECHNOLOGY
ISSN	0924-2244
卷号	122 页码:223-237
摘要	Background: Maintaining a healthy diet is vital to avoid health-related issues, e.g., undernutrition, obesity and many non-communicable diseases. An indispensable part of the health diet is dietary assessment. Traditional manual recording methods are not only burdensome but time-consuming, and contain substantial biases and errors. Recent advances in Artificial Intelligence (AI), especially computer vision technologies, have made it possible to develop automatic dietary assessment solutions, which are more convenient, less time-consuming and even more accurate to monitor daily food intake.Scope and approach: This review presents Vision-Based Dietary Assessment (VBDA) architectures, including multi-stage architecture and end-to-end one. The multi-stage dietary assessment generally consists of three stages: food image analysis, volume estimation and nutrient derivation. The prosperity of deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition. The recently proposed end-to-end methods are also discussed. We further analyze existing dietary assessment datasets, indicating that one large-scale benchmark is urgently needed, and finally highlight critical challenges and future trends for VBDA.Key findings and conclusions: After thorough exploration, we find that multi-task end-to-end deep learning approaches are one important trend of VBDA. Despite considerable research progress, many challenges remain for VBDA due to the meal complexity. We also provide the latest ideas for future development of VBDA, e.g., fine-grained food analysis and accurate volume estimation. This review aims to encourage researchers to propose more practical solutions for VBDA.
关键词	Dietary assessment Computer vision Deep learning Food recognition Food segmentation Volume estimation
DOI	10.1016/j.tifs.2022.02.017
收录类别	SCI
语种	英语
资助项目	Scientific Research Program of Bei-jing Municipal Education Commission[KZ202110011017] ; National Natural Science Foundation of China[61877002] ; National Natural Science Foundation of China[61972378] ; National Natural Science Foundation of China[U1936203] ; National Natural Science Foundation of China[U19B2040] ; Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety[BTBD-2020KF04]
WOS研究方向	Food Science & Technology
WOS类目	Food Science & Technology
WOS记录号	WOS:000788127400007
出版者	ELSEVIER SCIENCE LONDON
引用统计	被引频次：35[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18869
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Min, Weiqing; Li, Haisheng
作者单位	1.Beijing Technol & Business Univ, Sch Comp & Engn, Beijing 100048, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China 4.Natl Engn Lab Agriprod Qual Traceabil, Beijing 100048, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Wang, Wei,Min, Weiqing,Li, Tianhao,et al. A review on vision-based analysis for automatic dietary assessment[J]. TRENDS IN FOOD SCIENCE & TECHNOLOGY,2022,122:223-237.
APA	Wang, Wei,Min, Weiqing,Li, Tianhao,Dong, Xiaoxiao,Li, Haisheng,&Jiang, Shuqiang.(2022).A review on vision-based analysis for automatic dietary assessment.TRENDS IN FOOD SCIENCE & TECHNOLOGY,122,223-237.
MLA	Wang, Wei,et al."A review on vision-based analysis for automatic dietary assessment".TRENDS IN FOOD SCIENCE & TECHNOLOGY 122(2022):223-237.