Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patcog.2012.05.019
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dc.titleAttribute-restricted latent topic model for person re-identification
dc.contributor.authorLiu, X.
dc.contributor.authorSong, M.
dc.contributor.authorZhao, Q.
dc.contributor.authorTao, D.
dc.contributor.authorChen, C.
dc.contributor.authorBu, J.
dc.date.accessioned2014-10-07T04:24:05Z
dc.date.available2014-10-07T04:24:05Z
dc.date.issued2012-12
dc.identifier.citationLiu, X., Song, M., Zhao, Q., Tao, D., Chen, C., Bu, J. (2012-12). Attribute-restricted latent topic model for person re-identification. Pattern Recognition 45 (12) : 4204-4213. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patcog.2012.05.019
dc.identifier.issn00313203
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/81991
dc.description.abstractSearching 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.patcog.2012.05.019
dc.sourceScopus
dc.subjectAttribute-restricted latent topic model
dc.subjectPerson re-identification
dc.subjectSemantic topic
dc.subjectVisual attribute
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1016/j.patcog.2012.05.019
dc.description.sourcetitlePattern Recognition
dc.description.volume45
dc.description.issue12
dc.description.page4204-4213
dc.description.codenPTNRA
dc.identifier.isiut000308271000012
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