Learning Part-based Convolutional Features for Person Re-identification

Sun, Y; Zheng, L; Li, Y; Yang, Y; Tian, Q; Wang, S

Learning Part-based Convolutional Features for Person Re-identification

Sun, Y Zheng, L Li, Y Yang, Y

Tian, Q Wang, S

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43, (3), pp. 902-917
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Sun, Y
dc.contributor.author	Zheng, L
dc.contributor.author	Li, Y
dc.contributor.author	Yang, Y https://orcid.org/0000-0002-0512-880X
dc.contributor.author	Tian, Q
dc.contributor.author	Wang, S
dc.date.accessioned	2022-05-07T23:52:53Z
dc.date.available	2022-05-07T23:52:53Z
dc.date.issued	2021
dc.identifier.citation	IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43, (3), pp. 902-917
dc.identifier.issn	0162-8828
dc.identifier.issn	1939-3539
dc.identifier.uri	http://hdl.handle.net/10453/157121
dc.description.abstract	Part-level features offer fine granularity for pedestrian image description. In this article, we generally aim to learn discriminative part-informed feature for person re-identification. Our contribution is two-fold. First, we introduce a general part-level feature learning method, named Part-based Convolutional Baseline (PCB). Given an image input, it outputs a convolutional descriptor consisting of several part-level features. PCB is general in that it is able to accommodate several part partitioning strategies, including pose estimation, human parsing and uniform part partitioning. In experiment, we show that the learned descriptor has a significantly higher discriminative ability than the global descriptor. Second, based on PCB, we propose refined part pooling (RPP), which allows the parts to be more precisely located. Our idea is that pixels within a well-located part should be similar to each other while being dissimilar with pixels from other parts. We call it within-part consistency . When a pixel-wise feature vector in a part is more similar to some other part, it is then an outlier, indicating inappropriate partitioning. RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency. RPP requires no part labels and is trained in a weakly supervised manner. Experiment confirms that RPP allows PCB to gain another round of performance boost. For instance, on the Market-1501 dataset, we achieve (77.4+4.2) percent mAP and (92.3+1.5) percent rank-1 accuracy, a competitive performance with the state of the art.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Pattern Analysis and Machine Intelligence
dc.relation.isbasedon	10.1109/tpami.2019.2938523
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Learning Part-based Convolutional Features for Person Re-identification
dc.type	Journal Article
utslib.citation.volume	43
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-05-07T23:52:50Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	43
utslib.citation.issue	3

Abstract:

Part-level features offer fine granularity for pedestrian image description. In this article, we generally aim to learn discriminative part-informed feature for person re-identification. Our contribution is two-fold. First, we introduce a general part-level feature learning method, named Part-based Convolutional Baseline (PCB). Given an image input, it outputs a convolutional descriptor consisting of several part-level features. PCB is general in that it is able to accommodate several part partitioning strategies, including pose estimation, human parsing and uniform part partitioning. In experiment, we show that the learned descriptor has a significantly higher discriminative ability than the global descriptor. Second, based on PCB, we propose refined part pooling (RPP), which allows the parts to be more precisely located. Our idea is that pixels within a well-located part should be similar to each other while being dissimilar with pixels from other parts. We call it within-part consistency . When a pixel-wise feature vector in a part is more similar to some other part, it is then an outlier, indicating inappropriate partitioning. RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency. RPP requires no part labels and is trained in a weakly supervised manner. Experiment confirms that RPP allows PCB to gain another round of performance boost. For instance, on the Market-1501 dataset, we achieve (77.4+4.2) percent mAP and (92.3+1.5) percent rank-1 accuracy, a competitive performance with the state of the art.

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http://hdl.handle.net/10453/157121