Part-Based Deep Hashing for Large-Scale Person Re-Identification

Zhu, F; Kong, X; Zheng, L; Fu, H; Tian, Q

Part-Based Deep Hashing for Large-Scale Person Re-Identification

Zhu, F Kong, X Zheng, L

Fu, H Tian, Q

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2017, 26 (10), pp. 4806 - 4817
Issue Date:: 2017-10-01

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Field	Value	Language
dc.contributor.author	Zhu, F	en_US
dc.contributor.author	Kong, X	en_US
dc.contributor.author	Zheng, L https://orcid.org/0000-0002-1464-9500	en_US
dc.contributor.author	Fu, H	en_US
dc.contributor.author	Tian, Q	en_US
dc.date.issued	2017-10-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2017, 26 (10), pp. 4806 - 4817	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118092
dc.description.abstract	© 1992-2012 IEEE. Large-scale is a trend in person re-identi-fication (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accuracy for large-scale person re-id. We integrate spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing (PDH) is proposed, in which batches of triplet samples are employed as the input of the deep hashing architecture. Each triplet sample contains two pedestrian images (or parts) with the same identity and one pedestrian image (or part) of the different identity. A triplet loss function is employed with a constraint that the Hamming distance of pedestrian images (or parts) with the same identity is smaller than ones with the different identity. In the experiment, we show that the proposed PDH method yields very competitive re-id accuracy on the large-scale Market-1501 and Market-1501+500K datasets.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2017.2695101	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Part-Based Deep Hashing for Large-Scale Person Re-Identification	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	26	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 1992-2012 IEEE. Large-scale is a trend in person re-identi-fication (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accuracy for large-scale person re-id. We integrate spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing (PDH) is proposed, in which batches of triplet samples are employed as the input of the deep hashing architecture. Each triplet sample contains two pedestrian images (or parts) with the same identity and one pedestrian image (or part) of the different identity. A triplet loss function is employed with a constraint that the Hamming distance of pedestrian images (or parts) with the same identity is smaller than ones with the different identity. In the experiment, we show that the proposed PDH method yields very competitive re-id accuracy on the large-scale Market-1501 and Market-1501+500K datasets.

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http://hdl.handle.net/10453/118092