Random sampling based SVM for relevance feedback image retrieval

Tao, D; Tang, X

Random sampling based SVM for relevance feedback image retrieval

Tao, D

Tang, X

Permalink

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

Closed Access

	Filename	Description	Size
	2011001291OK.pdf		322.97 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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/10453/22292
dc.description.abstract	Relevance feedback (RF) schemes based on support vector machine (SVM) have been widely used in content-based image retrieval. However, the performance of SVM based RF is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1. SVM classifier is unstable on small size training set; 2. SVM's optimal hyper-plane may be biased when the positive feedback samples are much less than the negative feedback samples; 3. overfitting due to that the feature dimension is much higher than the size of the training set. In this paper, we try to use random sampling techniques to overcome these problems. To address the first two problems, we propose an asymmetric bagging based SVM. For the third problem, we combine the random subspace method (RSM) and SVM for RF. Finally, by integrating bagging and RSM, we solve all the three problems and further improve the RF performance.	en_US
dc.relation.ispartof	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition	en_US
dc.title	Random sampling based SVM for 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
dc.location.activity	Omaha, Nebraska
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) schemes based on support vector machine (SVM) have been widely used in content-based image retrieval. However, the performance of SVM based RF is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1. SVM classifier is unstable on small size training set; 2. SVM's optimal hyper-plane may be biased when the positive feedback samples are much less than the negative feedback samples; 3. overfitting due to that the feature dimension is much higher than the size of the training set. In this paper, we try to use random sampling techniques to overcome these problems. To address the first two problems, we propose an asymmetric bagging based SVM. For the third problem, we combine the random subspace method (RSM) and SVM for RF. Finally, by integrating bagging and RSM, we solve all the three problems and further improve the RF performance.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/22292