Mean shift segmentation method based on hybridized particle swarm optimization

  • Authors:
  • Yanling Li;Gang Li

  • Affiliations:
  • College of Computer and Information Technology, Xinyang Normal University, Xinyang, China;College of Computer and Information Technology, Xinyang Normal University, Xinyang, China

  • Venue:
  • ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
  • Year:
  • 2010

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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.