Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm

  • Authors:

  • Swagatam Das;Sudeshna Sil

  • Affiliations:

  • Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India;Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

A modified differential evolution (DE) algorithm is presented for clustering the pixels of an image in the gray-scale intensity space. The algorithm requires no prior information about the number of naturally occurring clusters in the image. It uses a kernel induced similarity measure instead of the conventional sum-of-squares distance. Use of the kernel function makes it possible to partition data that is linearly non-separable and non hyper-spherical in the original input space, into homogeneous groups in a transformed high-dimensional feature space. A novel search-variable representation scheme is adopted for selecting the optimal number of clusters from several possible choices. Extensive performance comparison over a test-suite of 10 gray-scale images and objective comparison with manually segmented ground truth indicates that the proposed algorithm has an edge over a few state-of-the-art algorithms for automatic multi-class image segmentation.