A Image Segmentation Algorithm Based on Differential Evolution Particle Swarm Optimization Fuzzy C-Means Clustering

Jiansheng Liu1 and Shangping Qiao2

  1. College of Science, Jiangxi University of Science and Technology
    341000 Ganzhou, P. R. China
    jxgzjscn@126.com
  2. Graduate School, Jiangxi University of Science and Technology
    341000 Ganzhou, P. R. China
    qiaoshangping@163.com

Abstract

This paper presents a hybrid differential evolution, particle swarm optimization and fuzzy c-means clustering algorithm called DEPSO-FCM for image segmentation. By the use of the differential evolution (DE) algorithm and particle swarm optimization to solve the FCM image segmentation influenced by the initial cluster centers and easily into a local optimum. Empirical results show that the proposed DEPSO-FCM has strong anti-noise ability; it can improve FCM and get better image segmentation results. In particular, for the HSI color image segmentation, the DEPSO-FCM can effectively solve the instability of FCM and the error split because of the singularity of the H component.

Key words

differential evolution particle swarm optimization, fuzzy c-means clustering, image segmentation, HSI color space

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS141108031L

Publication information

Volume 12, Issue 2 (June 2015)
Year of Publication: 2015
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Liu, J., Qiao, S.: A Image Segmentation Algorithm Based on Differential Evolution Particle Swarm Optimization Fuzzy C-Means Clustering. Computer Science and Information Systems, Vol. 12, No. 2, 873–893. (2015)