Title :
Image Segmentation Based on Modified Particle Swarm Optimization and Fuzzy C-Means Clustering
Author_Institution :
Sch. of Comput. & Electron. Eng., Hunan Univ. of Commerce, Changsha, China
Abstract :
In order to solve the problems of the fuzzy C-means (FCM) clustering algorithm when it is applied to the image segmentation such as making itself easily traps into local optimum and huge calculation, an image segmentation algorithm based on the modified particle swarm optimization(MPSO) and FCM clustering algorithm is proposed. The simulation results and the comparison between the proposed algorithm and FCM algorithm indicate that the proposed algorithm can obtain better segmentation effects and excel the existing FCM algorithm in several performance, such as the average dispersion, the maximum intra-distance between pixel and their cluster center, and the minimum inter-distance between any pair of clusters.
Keywords :
fuzzy set theory; image segmentation; particle swarm optimisation; pattern clustering; FCM; MPSO; fuzzy C-means clustering; image segmentation; modified particle swarm optimization; Automation; Business; Clustering algorithms; Electron traps; Fuzzy control; Fuzzy sets; Image segmentation; Particle swarm optimization; Partitioning algorithms; Pixel; fuzzy C-means clustering; image segmentation; particle swarm algorithm;
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Conference_Location :
Changsha, Hunan
Print_ISBN :
978-0-7695-3804-4
DOI :
10.1109/ICICTA.2009.153