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Image Clustering Using Improved Particle Swarm Optimization

  • Thuy Xuan PhamEmail author
  • Patrick Siarry
  • Hamouche Oulhadj
Conference paper
  • 584 Downloads
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 221)

Abstract

In this paper, we propose an improvement method for image segmentation problem using particle swarm optimization (PSO) with a new objective function based on kernelization of improved fuzzy entropy clustering algorithm with spatial local information, called PSO-KFECS. The main objective of our proposed algorithm is to segment accurately images by utilizing the state-of-the-art development of PSO in optimization with a novel fitness function. The proposed PSO-KFECS was evaluated on several benchmark test images including synthetic images (http://pages.upf.pf/Sebastien.Chabrier/ressources.php), and simulated brain MRI images from the McConnell Brain Imaging Center (BrainWeb (http://brainweb.bic.mni.mcgill.ca/brainweb/)). Experimental results show that our proposed PSO-KFECS algorithm can perform better than the competing algorithms.

Keywords

Image segmentation Particle swarm optimization Entropy based fuzzy clustering Fitness function 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Thuy Xuan Pham
    • 1
    Email author
  • Patrick Siarry
    • 1
  • Hamouche Oulhadj
    • 1
  1. 1.Laboratory Images, Signals, and Intelligent Systems, University Paris-Est CreteilVitry sur SeineFrance

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