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Genetic FCMS Clustering Algorithm for Image Segmentation

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Green Communications and Networks

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 113))

Abstract

FCM is populated in image segmentation for its simplicity and easily realization. The classic FCM segmentation used only the gray value for segmentation, and is liable to stuck at local values, and the result is relied on cluster center of initial selection. In this paper, we present a Genetic fuzzy c-means (GFCMS) algorithm that incorporates spatial information for segmentation. The first improvement is to use the spatial information of pixel in FCM algorithm. The second improvement is to use the genetic algorithm for searching the global optimum. The results of the experiment validates that the algorithm has better adaptability and gets the correct global optimum.

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Author information

Authors and Affiliations

  1. College of Computer Science, Sichuan Panzhihua University, Panzhihua, 610007, Sichuan, China

    Chunyu Zhang & Cuiyin Liu

  2. International Baccalaureate Precollege Program 2009, The High School Affiliated to Nanjing Normal University, Nanjing, 210003, China

    Pengfei Wang

Authors
  1. Chunyu Zhang
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  2. Pengfei Wang
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  3. Cuiyin Liu
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Corresponding author

Correspondence to Chunyu Zhang .

Editor information

Editors and Affiliations

  1. , Department of Electronic Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 200240, China, People's Republic

    Yuhang Yang

  2. , Electrical & Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore

    Maode Ma

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© 2012 Springer Science+Business Media B.V.

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Zhang, C., Wang, P., Liu, C. (2012). Genetic FCMS Clustering Algorithm for Image Segmentation. In: Yang, Y., Ma, M. (eds) Green Communications and Networks. Lecture Notes in Electrical Engineering, vol 113. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2169-2_26

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  • DOI: https://doi.org/10.1007/978-94-007-2169-2_26

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-2168-5

  • Online ISBN: 978-94-007-2169-2

  • eBook Packages: EngineeringEngineering (R0)

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