Orthogonal complement component analysis for positive samples in SVM based relevance feedback image retrieval

Tao, D; Tang, X

Orthogonal complement component analysis for positive samples in SVM based relevance feedback image retrieval

Tao, D

Tang, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, 2
Issue Date:: 2004-10-19

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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.date.issued	2004-10-19	en_US
dc.identifier.citation	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, 2	en_US
dc.identifier.issn	1063-6919	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22293
dc.description.abstract	Relevance feedback (RF) is an important tool to improve the performance of content-based image retrieval system. Support vector machine (SVM) based RF is popular because it can generalize better than most other classifiers. However, directly using SVM in RF may not be appropriate, since SVM treats the positive and negative feedbacks equally. Given the different properties of positive samples and negative samples in RF, they should be treated differently. Considering this, we propose an orthogonal complement components analysis (OCCA) combined with SVM in this paper. We then generalize the OCCA to Hilbert space and define the kernel empirical OCCA (KEOCCA). Through experiments on a Corel Photo database with 17,800 images, we demonstrate that the proposed method can significantly improve the performance of conventional SVM-based RF.	en_US
dc.relation.ispartof	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition	en_US
dc.title	Orthogonal complement component analysis for positive samples in SVM based relevance feedback image retrieval	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Washington, DC	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.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

Relevance feedback (RF) is an important tool to improve the performance of content-based image retrieval system. Support vector machine (SVM) based RF is popular because it can generalize better than most other classifiers. However, directly using SVM in RF may not be appropriate, since SVM treats the positive and negative feedbacks equally. Given the different properties of positive samples and negative samples in RF, they should be treated differently. Considering this, we propose an orthogonal complement components analysis (OCCA) combined with SVM in this paper. We then generalize the OCCA to Hilbert space and define the kernel empirical OCCA (KEOCCA). Through experiments on a Corel Photo database with 17,800 images, we demonstrate that the proposed method can significantly improve the performance of conventional SVM-based RF.

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http://hdl.handle.net/10453/22293