• DocumentCode
    3625490
  • Title

    Standard and Genetic k-means Clustering Techniques in Image Segmentation

  • Author

    Dariusz Malyszko;Slawomir T. Wierzchon

  • Author_Institution
    Technical University of Bialystok, Poland
  • fYear
    2007
  • fDate
    6/1/2007 12:00:00 AM
  • Firstpage
    299
  • Lastpage
    304
  • Abstract
    Clustering or data grouping is a key initial procedure in image processing. This paper deals with the application of standard and genetic k-means clustering algorithms in the area of image segmentation. In order to assess and compare both versions of k-means algorithm and its variants, appropriate procedures and software have been designed and implemented. Experimental results point that genetically optimized k-means algorithms proved their usefulness in the area of image analysis, yielding comparable and even better segmentation results.
  • Keywords
    "Image segmentation","Clustering algorithms","Genetic algorithms","Partitioning algorithms","Application software","Algorithm design and analysis","Image analysis","Robustness","Data analysis","Iterative algorithms"
  • Publisher
    ieee
  • Conference_Titel
    Computer Information Systems and Industrial Management Applications, 2007. CISIM ´07. 6th International Conference on
  • Print_ISBN
    0-7695-2894-5
  • Type

    conf

  • DOI
    10.1109/CISIM.2007.63
  • Filename
    4273538