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Mean Shift Segmentation Method Based on Hybridized Particle Swarm Optimization

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Advances in Neural Networks - ISNN 2010 (ISNN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6064))

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Abstract

Mean shift, like other gradient ascent optimization methods, is susceptible to local maxima, and hence often fails to find the desired global maximum. In this paper, mean shift segmentation method based on hybridized particle swarm optimization algorithm is proposed which overcomes the shortcoming of mean shift. The mean shift vector is firstly optimized using hybridized PSO algorithm when performing the new algorithm. Then, the optimal mean shift vector is updated using mean shift procedure. Experimental results show that the proposed algorithm used for image segmentation can segment images more effectively and provide more robust segmentation results.

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Author information

Authors and Affiliations

  1. College of Computer and Information Technology, Xinyang Normal University, Xinyang, 464000, China

    Yanling Li & Gang Li

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yanling Li

Authors
  1. Yanling Li
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  2. Gang Li
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Liqing Zhang  & Bao-Liang Lu  & 

  2. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

    James Kwok

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© 2010 Springer-Verlag Berlin Heidelberg

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Li, Y., Li, G. (2010). Mean Shift Segmentation Method Based on Hybridized Particle Swarm Optimization. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_26

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  • DOI: https://doi.org/10.1007/978-3-642-13318-3_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13317-6

  • Online ISBN: 978-3-642-13318-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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