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Deep Hybrid Similarity Learning for Person Re-Identification
Zhu JQ(朱建清); Huanqiang Zeng; Liao SC(廖胜才); Lei Z(雷震); Canhui Cai; Li Xin Zheng
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN1051-8215
2018-11-01
Volume28Issue:11Pages:3183-3193
Corresponding AuthorZeng, Huanqiang(zeng0043@hqu.edu.cn)
AbstractPerson re-identification (Re-ID) aims to match person images captured from two non-overlapping cameras. In this paper, a deep hybrid similarity learning (DHSL) method for person Re-ID based on a convolution neural network (CNN) is proposed. In our approach, a light CNN learning feature pair for the input image pair is simultaneously extracted. Then, both the elementwise absolute difference and multiplication of the CNN learning feature pair are calculated. Finally, a hybrid similarity function is designed to measure the similarity between the feature pair, which is realized by learning a group of weight coefficients to project the elementwise absolute difference and multiplication into a similarity score. Consequently, the proposed DHSL method is able to reasonably assign complexities of feature learning and metric learning in a CNN, so that the performance of person Re-ID is improved. Experiments on three challenging person Re-ID databases, QMUL GRID, VIPeR, and CUHK03, illustrate that the proposed DHSL method is superior to multiple state-of-the-art person Re-ID methods.
KeywordMetric learning convolution neural network deep hybrid similarity learning person re-identification (Re-ID)
DOI10.1109/TCSVT.2017.2734740
WOS KeywordFEATURES ; VERIFICATION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61602191] ; National Natural Science Foundation of China[61672521] ; National Natural Science Foundation of China[61375037] ; National Natural Science Foundation of China[61473291] ; National Natural Science Foundation of China[61572501] ; National Natural Science Foundation of China[61572536] ; National Natural Science Foundation of China[61502491] ; National Natural Science Foundation of China[61372107] ; National Natural Science Foundation of China[61401167] ; Natural Science Foundation of Fujian Province[2016J01308] ; Scientific and Technology Founds of Xiamen[3502Z20173045] ; Promotion Program for Young and Middleaged Teacher in Science and Technology Research of Huaqiao University[ZQN-PY418] ; Promotion Program for Young and Middleaged Teacher in Science and Technology Research of Huaqiao University[ZQN-YX403] ; Scientific Research Funds of Huaqiao University[16BS108]
Funding OrganizationNational Natural Science Foundation of China ; Natural Science Foundation of Fujian Province ; Scientific and Technology Founds of Xiamen ; Promotion Program for Young and Middleaged Teacher in Science and Technology Research of Huaqiao University ; Scientific Research Funds of Huaqiao University
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000449392100008
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20621
Collection模式识别国家重点实验室_生物识别与安全技术研究
Recommended Citation
GB/T 7714
Zhu JQ,Huanqiang Zeng,Liao SC,et al. Deep Hybrid Similarity Learning for Person Re-Identification[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2018,28(11):3183-3193.
APA Zhu JQ,Huanqiang Zeng,Liao SC,Lei Z,Canhui Cai,&Li Xin Zheng.(2018).Deep Hybrid Similarity Learning for Person Re-Identification.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,28(11),3183-3193.
MLA Zhu JQ,et al."Deep Hybrid Similarity Learning for Person Re-Identification".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 28.11(2018):3183-3193.
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