KM and KHM Clustering Techniques for Colour Image Quantisation

  • Mariusz FrackiewiczEmail author
  • Henryk Palus
Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 19)


This paper deals with the comparison of two clustering techniques kmeans (KM) and k-harmonic means (KHM) in the case of their use in colour image quantisation. The classical KMtechnique establishes good background for this comparison. Authors proposed two original heuristic initialisation methods, one arbitrary(DC) and one adaptive (SD), that were used in both techniques. Apart from specific validity indices for clustering, the results were also evaluated by means of average colour differences in RGB (PSNR) and CIELAB colour space (ΔE) and additionally difference of colourfulness (ΔM). Experimental tests realised on benchmark colour images show the superiority of KHMover KM. Other problems with both clustering techniques e.g. empty clusters have also been highlighted.


Colour image quantisation k-means k-harmonic means 



This work has been supported by the Polish Ministry of Science and Higher Education under RD grant no. N N516 374736 from the Science Budget 2009–2011.


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Silesian University of TechnologyGliwicePoland

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