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Mixed Dish Recognition With Contextual Relation and Domain Alignment

文献类型：期刊论文

作者Deng, Lixi1,3,6; Chen, Jingjing4; Ngo, Chong-Wah5; Sun, Qianru5; Tang, Sheng6; Zhang, Yongdong7; Chua, Tat-Seng2
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2022
卷号24页码:2034-2045
关键词Visualization Semantics Feature extraction Image recognition Training Testing Context modeling Mixed dish recognition Contextual relation Domain alignment
ISSN号1520-9210
DOI10.1109/TMM.2021.3075037
英文摘要Mixed dish is a food category that contains different dishes mixed in one plate, and is popular in Eastern and Southeast Asia. Recognizing the individual dishes in a mixed dish image is important for health related applications, e.g. to calculate the nutrition values of the dish. However, most existing methods that focus on single dish classification are not applicable to the recognition of mixed dish images. The main challenge of mixed dish recognition comes from three aspects: a wide range of dish types, the complex dish combination with severe overlap between different dishes and the large visual variances of same dish type caused by different cooking/cutting methods applied in different canteens. In order to tackle these problems, we propose the contextual relation network that encodes the implicit and explicit contextual relations among multiple dishes from region-level features and label-level co-occurrence respectively. Besides, to address the visual variances of dish instances from different canteens, we introduce the domain adaption networks to align both local and global features, and eliminating domain gaps of dish features across different canteens. In addition, we collect a mixed dish image dataset containing 9254 mixed dish images from 6 canteens in Singapore. Extensive experiments on both our dataset and public one validate that our methods can achieve top performance for localizing and recognizing multiple dishes and solve the domain shift problem to a certain extent in mixed dish images.
资助项目National Key Research and Development Program of China[2017YFB1002202] ; A*STAR under its AME YIRG[A20E6c0101] ; National Natural Science Foundation of China[61871004] ; National Natural Science Foundation of China[2020A077] ; Sea-NExT Joint Lab
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000778959200020
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/18886]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chen, Jingjing
作者单位1.JD Com, Beijing 100049, Peoples R China
2.Natl Univ Singapore, Singapore 117543, Singapore
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Fudan Univ, Shanghai Key Labortaory Intelligent Informat Proc, Sch Comp Sci, Shanghai 200433, Peoples R China
5.Singapore Management Univ, Sch Comp & Informat Syst, Singapore 188065, Singapore
6.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
7.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230022, Peoples R China
推荐引用方式
GB/T 7714		 Deng, Lixi,Chen, Jingjing,Ngo, Chong-Wah,et al. Mixed Dish Recognition With Contextual Relation and Domain Alignment[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2022,24:2034-2045.
APA Deng, Lixi.,Chen, Jingjing.,Ngo, Chong-Wah.,Sun, Qianru.,Tang, Sheng.,...&Chua, Tat-Seng.(2022).Mixed Dish Recognition With Contextual Relation and Domain Alignment.IEEE TRANSACTIONS ON MULTIMEDIA,24,2034-2045.
MLA Deng, Lixi,et al."Mixed Dish Recognition With Contextual Relation and Domain Alignment".IEEE TRANSACTIONS ON MULTIMEDIA 24(2022):2034-2045.

入库方式： OAI收割

来源：计算技术研究所

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