Biased isomap projections for interactive reranking

Bian, W; Cheng, J; Tao, D

Biased isomap projections for interactive reranking

Bian, W Cheng, J Tao, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009, 2009, pp. 1632 - 1635
Issue Date:: 2009-11-20

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Field	Value	Language
dc.contributor.author	Bian, W	en_US
dc.contributor.author	Cheng, J	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2009-11-20	en_US
dc.identifier.citation	Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009, 2009, pp. 1632 - 1635	en_US
dc.identifier.isbn	9781424442911	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17289
dc.description.abstract	Image search has recently gained more and more attention for various applications. To capture users' intensions and to bridge the gap between the low level visual features and the high level semantics, a dozen of interactive reranking (IR) or relevance feedback (RF) algorithms have been developed and achieved significant performance improvements. In this paper, we develop a novel subspace learning based IR algorithm by using the patch alignment framework, termed the biased ISOMap projections or BIP for short. BIP models both the intraclass local geometry for query relevant images and the interclass discrimination between query relevant images and irrelevant images. In addition, BIP never meets the small samples size problem. We present experimental evidence suggesting that BIP is effective for targeting the intensions of users and reducing the semantic gaps for image search. ©2009 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009	en_US
dc.relation.isbasedon	10.1109/ICME.2009.5202832	en_US
dc.title	Biased isomap projections for interactive reranking	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	New York, NY	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Image search has recently gained more and more attention for various applications. To capture users' intensions and to bridge the gap between the low level visual features and the high level semantics, a dozen of interactive reranking (IR) or relevance feedback (RF) algorithms have been developed and achieved significant performance improvements. In this paper, we develop a novel subspace learning based IR algorithm by using the patch alignment framework, termed the biased ISOMap projections or BIP for short. BIP models both the intraclass local geometry for query relevant images and the interclass discrimination between query relevant images and irrelevant images. In addition, BIP never meets the small samples size problem. We present experimental evidence suggesting that BIP is effective for targeting the intensions of users and reducing the semantic gaps for image search. ©2009 IEEE.

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http://hdl.handle.net/10453/17289