中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
A review on vision-based analysis for automatic dietary assessment

文献类型：期刊论文

作者Wang, Wei1,3,4; Min, Weiqing2,5; Li, Tianhao2,5; Dong, Xiaoxiao1,3,4; Li, Haisheng1,3,4; Jiang, Shuqiang2,5
刊名TRENDS IN FOOD SCIENCE & TECHNOLOGY
出版日期2022-04-01
卷号122页码:223-237
关键词Dietary assessment Computer vision Deep learning Food recognition Food segmentation Volume estimation
ISSN号0924-2244
DOI10.1016/j.tifs.2022.02.017
英文摘要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.
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
语种英语
WOS记录号WOS:000788127400007
出版者ELSEVIER SCIENCE LONDON
源URL[http://119.78.100.204/handle/2XEOYT63/18868]  
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。