KPCA for semantic object extraction in images

Li, J; Li, X; Tao, D

KPCA for semantic object extraction in images

Li, J Li, X Tao, D

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Publication Type:: Journal Article
Citation:: Pattern Recognition, 2008, 41 (10), pp. 3244 - 3250
Issue Date:: 2008-10-01

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Field	Value	Language
dc.contributor.author	Li, J	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2008-10-01	en_US
dc.identifier.citation	Pattern Recognition, 2008, 41 (10), pp. 3244 - 3250	en_US
dc.identifier.issn	0031-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15167
dc.description.abstract	In this paper, we kernelize conventional clustering algorithms from a novel point of view. Based on the fully mathematical proof, we first demonstrate that kernel KMeans (KKMeans) is equivalent to kernel principal component analysis (KPCA) prior to the conventional KMeans algorithm. By using KPCA as a preprocessing step, we also generalize Gaussian mixture model (GMM) to its kernel version, the kernel GMM (KGMM). Consequently, conventional clustering algorithms can be easily kernelized in the linear feature space instead of a nonlinear one. To evaluate the newly established KKMeans and KGMM algorithms, we utilized them to the problem of semantic object extraction (segmentation) of color images. Based on a series of experiments carried out on a set of color images, we indicate that both KKMeans and KGMM can offer more elaborate output than the conventional KMeans and GMM, respectively. © 2008 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Pattern Recognition	en_US
dc.relation.isbasedon	10.1016/j.patcog.2008.03.018	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	KPCA for semantic object extraction in images	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	41	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	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	41	en_US

Abstract:

In this paper, we kernelize conventional clustering algorithms from a novel point of view. Based on the fully mathematical proof, we first demonstrate that kernel KMeans (KKMeans) is equivalent to kernel principal component analysis (KPCA) prior to the conventional KMeans algorithm. By using KPCA as a preprocessing step, we also generalize Gaussian mixture model (GMM) to its kernel version, the kernel GMM (KGMM). Consequently, conventional clustering algorithms can be easily kernelized in the linear feature space instead of a nonlinear one. To evaluate the newly established KKMeans and KGMM algorithms, we utilized them to the problem of semantic object extraction (segmentation) of color images. Based on a series of experiments carried out on a set of color images, we indicate that both KKMeans and KGMM can offer more elaborate output than the conventional KMeans and GMM, respectively. © 2008 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/15167