Fuzzy C-Means Clustering Algorithm for Image Segmentation Based on Improved Particle Swarm Optimization

Abstract:

A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

• Periodical: Advanced Materials Research (Volumes 532-533)
• Pages: 1553-1557
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.532-533.1553
• Online since: June 2012
• Authors: Yue Yang, Shu Xu Guo, Run Lan Tian, Peng Liu
• Keywords: Fuzzy C-Means Clustering, Image Segmentation, Particle Swarm Optimization Algorithm (PSO)

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