Direct kernel biased discriminant analysis: A new content-based image retrieval relevance feedback algorithm

Tao, D; Tang, X; Li, X; Rui, Y

Direct kernel biased discriminant analysis: A new content-based image retrieval relevance feedback algorithm

Tao, D

Tang, X Li, X Rui, Y

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Multimedia, 2006, 8 (4), pp. 716 - 727
Issue Date:: 2006-08-01

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Field	Value	Language
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Tang, X	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Rui, Y	en_US
dc.date.issued	2006-08-01	en_US
dc.identifier.citation	IEEE Transactions on Multimedia, 2006, 8 (4), pp. 716 - 727	en_US
dc.identifier.issn	1520-9210	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15089
dc.description.abstract	In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms. © 2006 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Multimedia	en_US
dc.relation.isbasedon	10.1109/TMM.2005.861375	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Direct kernel biased discriminant analysis: A new content-based image retrieval relevance feedback algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	8	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 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	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms. © 2006 IEEE.

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http://hdl.handle.net/10453/15089