Deep Hybrid Similarity Learning for Person Re-Identification

doi:10.1109/TCSVT.2017.2734740

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	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
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN	1051-8215
	2018-11-01
卷号	28 期号:11 页码:3183-3193
通讯作者	Zeng, Huanqiang(zeng0043@hqu.edu.cn)
摘要	Person 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.
关键词	Metric learning convolution neural network deep hybrid similarity learning person re-identification (Re-ID)
DOI	10.1109/TCSVT.2017.2734740
关键词[WOS]	FEATURES ; VERIFICATION
收录类别	SCI
语种	英语
资助项目	Scientific Research Funds of Huaqiao University[16BS108] ; Promotion Program for Young and Middleaged Teacher in Science and Technology Research of Huaqiao University[ZQN-YX403] ; Promotion Program for Young and Middleaged Teacher in Science and Technology Research of Huaqiao University[ZQN-PY418] ; Scientific and Technology Founds of Xiamen[3502Z20173045] ; Natural Science Foundation of Fujian Province[2016J01308] ; National Natural Science Foundation of China[61401167] ; National Natural Science Foundation of China[61372107] ; National Natural Science Foundation of China[61502491] ; National Natural Science Foundation of China[61572536] ; National Natural Science Foundation of China[61572501] ; National Natural Science Foundation of China[61473291] ; National Natural Science Foundation of China[61375037] ; National Natural Science Foundation of China[61672521] ; National Natural Science Foundation of China[61602191] ; National 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]
项目资助者	National 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研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000449392100008
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：51[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20621
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
推荐引用方式 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.