Attribute-restricted latent topic model for person re-identification

Liu, X; Song, M; Zhao, Q; Tao, D; Chen, C; Bu, J

Attribute-restricted latent topic model for person re-identification

Liu, X Song, M Zhao, Q Tao, D

Chen, C Bu, J

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Publication Type:: Journal Article
Citation:: Pattern Recognition, 2012, 45 (12), pp. 4204 - 4213
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Liu, X	en_US
dc.contributor.author	Song, M	en_US
dc.contributor.author	Zhao, Q	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Chen, C	en_US
dc.contributor.author	Bu, J	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Pattern Recognition, 2012, 45 (12), pp. 4204 - 4213	en_US
dc.identifier.issn	0031-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22868
dc.description.abstract	Searching for specific persons from surveillance videos captured by different cameras, known as person re-identification, is a key yet under-addressed challenge. Difficulties arise from the large variations of human appearance in different poses, from the different camera views that may be involved, making low-level descriptor representation unreliable. In this paper, we propose a novel Attribute-Restricted Latent Topic Model (ARLTM) to encode targets into semantic topics. Compared to conventional topic models such as LDA and pLSI, ARLTM performs best by imposing semantic restrictions onto the generation of human specific attributes. We use MCMC EM for model learning. Experimental results show that our method achieves state-of-the-art performance. © 2012 Elsevier Ltd.	en_US
dc.relation.ispartof	Pattern Recognition	en_US
dc.relation.isbasedon	10.1016/j.patcog.2012.05.019	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Attribute-restricted latent topic model for person re-identification	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	45	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	45	en_US

Abstract:

Searching for specific persons from surveillance videos captured by different cameras, known as person re-identification, is a key yet under-addressed challenge. Difficulties arise from the large variations of human appearance in different poses, from the different camera views that may be involved, making low-level descriptor representation unreliable. In this paper, we propose a novel Attribute-Restricted Latent Topic Model (ARLTM) to encode targets into semantic topics. Compared to conventional topic models such as LDA and pLSI, ARLTM performs best by imposing semantic restrictions onto the generation of human specific attributes. We use MCMC EM for model learning. Experimental results show that our method achieves state-of-the-art performance. © 2012 Elsevier Ltd.

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http://hdl.handle.net/10453/22868