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Image Clustering Using Improved Particle Swarm Optimization

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Industrial Networks and Intelligent Systems (INISCOM 2017)

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.

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Authors and Affiliations

  1. Laboratory Images, Signals, and Intelligent Systems, University Paris-Est Creteil, 94400, Vitry sur Seine, France

    Thuy Xuan Pham, Patrick Siarry & Hamouche Oulhadj

Authors
  1. Thuy Xuan Pham
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  2. Patrick Siarry
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  3. Hamouche Oulhadj
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Corresponding author

Correspondence to Thuy Xuan Pham .

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Editors and Affiliations

  1. Hangzhou Dianzi University, Hangzhou, China

    Yuanfang Chen

  2. Queen’s University Belfast, Belfast, United Kingdom

    Trung Q. Duong

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

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Pham, T.X., Siarry, P., Oulhadj, H. (2018). Image Clustering Using Improved Particle Swarm Optimization. In: Chen, Y., Duong, T. (eds) Industrial Networks and Intelligent Systems. INISCOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 221. Springer, Cham. https://doi.org/10.1007/978-3-319-74176-5_31

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  • DOI: https://doi.org/10.1007/978-3-319-74176-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74175-8

  • Online ISBN: 978-3-319-74176-5

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