Title :
Image clustering using Particle Swarm Optimization
Author :
Wong, Man To ; He, Xiangjian ; Yeh, Wei-Chang
Author_Institution :
Centre for Innovation in IT Services & Applic., Univ. of Technol., Sydney Broadway, NSW, Australia
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.
Keywords :
image segmentation; particle swarm optimisation; pattern clustering; K-means clustering; PSO clustering algorithm; fitness function; image clustering algorithm; intercluster separation; particle swarm optimization; Airplanes; Clustering algorithms; Equations; Mathematical model; Partitioning algorithms; Pixel; Quantization; K-means clustering; image clustering; particle swarm optimization; partitional clustering;
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
Print_ISBN :
978-1-4244-7834-7
DOI :
10.1109/CEC.2011.5949627
