This paper presents a new approach of dynamic clustering based on improved Particle Swarm Optimization (PSO) and which is applied to image segmentation (called DCPSONS). Firstly, the original PSO algorithm is improved by using diversity mechanism and neighborhood search strategy. The improved PSO is then combined with the well-known data clustering k-means algorithm for dynamic clustering problem where the number of clusters has not yet been known. Finally, DCPSONS is applied to image segmentation problem, in which the number of clusters is automatically determined. Experimental results in using sixteen benchmark data sets and several images of synthetic and natural benchmark data demonstrate that the proposed DCPSONS algorithm substantially outperforms other competitive algorithms in terms of accuracy and convergence rate.
Publié le : 2017-07-06
Classification:
Knowledge and Information Engineering; other areas of Computing and Informatics,
Particle swarm optimization, neighborhood search, diversity, global optimization, dynamic clustering, image segmentation,
68T01
@article{cai2017_3_637,
author = {Dang Cong Tran; State Key Lab of Software Engineering, Wuhan University, Wuhan 430072 and Zhijian Wu; State Key Lab of Software Engineering, Wuhan University},
title = {A New Approach of Dynamic Clustering Based on Particle Swarm Optimization and Application in Image Segmentation},
journal = {Computing and Informatics},
volume = {35},
number = {4},
year = {2017},
language = {en},
url = {http://dml.mathdoc.fr/item/cai2017_3_637}
}
Dang Cong Tran; State Key Lab of Software Engineering, Wuhan University, Wuhan 430072; Zhijian Wu; State Key Lab of Software Engineering, Wuhan University. A New Approach of Dynamic Clustering Based on Particle Swarm Optimization and Application in Image Segmentation. Computing and Informatics, Tome 35 (2017) no. 4, . http://gdmltest.u-ga.fr/item/cai2017_3_637/