Image clustering using Particle Swarm Optimization

Wong, MT; He, X; Yeh, WC

Image clustering using Particle Swarm Optimization

Wong, MT He, X

Yeh, WC

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Publication Type:: Conference Proceeding
Citation:: 2011 IEEE Congress of Evolutionary Computation, CEC 2011, 2011, pp. 262 - 268
Issue Date:: 2011-08-29

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Field	Value	Language
dc.contributor.author	Wong, MT	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Yeh, WC https://orcid.org/0000-0001-7393-0768	en_US
dc.date.issued	2011-08-29	en_US
dc.identifier.citation	2011 IEEE Congress of Evolutionary Computation, CEC 2011, 2011, pp. 262 - 268	en_US
dc.identifier.isbn	9781424478347	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29560
dc.description.abstract	This paper proposes an image clustering algorithm using Particle Swarm Optimization (PSO) with two improved fitness functions. The PSO clustering algorithm can be used to find centroids of a user specified number of clusters. Two new fitness functions are proposed in this paper. The PSO-based image clustering algorithm with the proposed fitness functions is compared to the K-means clustering. Experimental results show that the PSO-based image clustering approach, using the improved fitness functions, can perform better than K-means by generating more compact clusters and larger inter-cluster separation. © 2011 IEEE.	en_US
dc.relation.ispartof	2011 IEEE Congress of Evolutionary Computation, CEC 2011	en_US
dc.relation.isbasedon	10.1109/CEC.2011.5949627	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Image clustering using Particle Swarm Optimization	en_US
dc.type	Conference Proceeding
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
dc.location.activity	New Orleans LA	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper proposes an image clustering algorithm using Particle Swarm Optimization (PSO) with two improved fitness functions. The PSO clustering algorithm can be used to find centroids of a user specified number of clusters. Two new fitness functions are proposed in this paper. The PSO-based image clustering algorithm with the proposed fitness functions is compared to the K-means clustering. Experimental results show that the PSO-based image clustering approach, using the improved fitness functions, can perform better than K-means by generating more compact clusters and larger inter-cluster separation. © 2011 IEEE.

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http://hdl.handle.net/10453/29560