Visual search reranking via adaptive particle swarm optimization

Zhang, L; Mei, T; Liu, Y; Tao, D; Zhou, HQ

Visual search reranking via adaptive particle swarm optimization

Zhang, L Mei, T Liu, Y Tao, D

Zhou, HQ

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Publication Type:: Journal Article
Citation:: Pattern Recognition, 2011, 44 (8), pp. 1811 - 1820
Issue Date:: 2011-08-01

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Field	Value	Language
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Mei, T	en_US
dc.contributor.author	Liu, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Zhou, HQ	en_US
dc.date.issued	2011-08-01	en_US
dc.identifier.citation	Pattern Recognition, 2011, 44 (8), pp. 1811 - 1820	en_US
dc.identifier.issn	0031-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18251
dc.description.abstract	Visual search reranking involves an optimization process that uses visual content to recover the genuine ranking list from the helpful but noisy one generated by textual search. This paper presents an evolutionary approach, called Adaptive Particle Swarm Optimization (APSO), for unsupervised visual search reranking. The proposed approach incorporates the visual consistency regularization and the ranking list distance. In addition, to address the problem that existing list distance fails to capture the genuine disagreement between two ranking lists, we propose a numerical ranking list distance. Furthermore, the parameters in APSO are self-tuned adaptively according to the fitness values of the particles to avoid being trapped in local optima. We conduct extensive experiments on automatic search task over TRECVID 20062007 benchmarks and show significant and consistent improvements over state-of-the-art works. © 2011 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Pattern Recognition	en_US
dc.relation.isbasedon	10.1016/j.patcog.2011.01.016	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Visual search reranking via adaptive particle swarm optimization	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	44	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	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	44	en_US

Abstract:

Visual search reranking involves an optimization process that uses visual content to recover the genuine ranking list from the helpful but noisy one generated by textual search. This paper presents an evolutionary approach, called Adaptive Particle Swarm Optimization (APSO), for unsupervised visual search reranking. The proposed approach incorporates the visual consistency regularization and the ranking list distance. In addition, to address the problem that existing list distance fails to capture the genuine disagreement between two ranking lists, we propose a numerical ranking list distance. Furthermore, the parameters in APSO are self-tuned adaptively according to the fitness values of the particles to avoid being trapped in local optima. We conduct extensive experiments on automatic search task over TRECVID 20062007 benchmarks and show significant and consistent improvements over state-of-the-art works. © 2011 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/18251