Image clustering using Particle Swarm Optimization

Man To Wong, Xiangjian He, Wei Chang Yeh

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

26 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2011 IEEE Congress of Evolutionary Computation, CEC 2011
Number of pages7
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE Congress of Evolutionary Computation, CEC 2011 - New Orleans, LA, United States
Duration: 5 Jun 20118 Jun 2011

Publication series

Name2011 IEEE Congress of Evolutionary Computation, CEC 2011


Conference2011 IEEE Congress of Evolutionary Computation, CEC 2011
Country/TerritoryUnited States
CityNew Orleans, LA


  • image clustering
  • K-means clustering
  • particle swarm optimization
  • partitional clustering

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science


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