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.
| Original language | English |
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| Title of host publication | 2011 IEEE Congress of Evolutionary Computation, CEC 2011 |
| Pages | 262-268 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 2011 IEEE Congress of Evolutionary Computation, CEC 2011 - New Orleans, LA, United States Duration: 5 Jun 2011 → 8 Jun 2011 |
Publication series
| Name | 2011 IEEE Congress of Evolutionary Computation, CEC 2011 |
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Conference
| Conference | 2011 IEEE Congress of Evolutionary Computation, CEC 2011 |
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| Country/Territory | United States |
| City | New Orleans, LA |
| Period | 5/06/11 → 8/06/11 |
Keywords
- K-means clustering
- image clustering
- particle swarm optimization
- partitional clustering
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Theoretical Computer Science
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Wong, M. T., He, X., & Yeh, W. C. (2011). Image clustering using Particle Swarm Optimization. In 2011 IEEE Congress of Evolutionary Computation, CEC 2011 (pp. 262-268). Article 5949627 (2011 IEEE Congress of Evolutionary Computation, CEC 2011). https://doi.org/10.1109/CEC.2011.5949627