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KM and KHM Clustering Techniques for Colour Image Quantisation

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Computational Vision and Medical Image Processing

Part of the book series: Computational Methods in Applied Sciences ((COMPUTMETHODS,volume 19))

Abstract

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.

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Acknowledgements

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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Authors and Affiliations

  1. Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Mariusz Frackiewicz & Henryk Palus

Authors
  1. Mariusz Frackiewicz
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  2. Henryk Palus
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Correspondence to Mariusz Frackiewicz .

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Editors and Affiliations

  1. Fac. Engenharia, Lab. Engenharia de Processos,, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal

    João Manuel R. S. Tavares

  2. Fac. Engenharia, Lab. Engenharia de Processos,, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal

    R. M. Natal Jorge

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Frackiewicz, M., Palus, H. (2011). KM and KHM Clustering Techniques for Colour Image Quantisation. In: Tavares, J., Jorge, R. (eds) Computational Vision and Medical Image Processing. Computational Methods in Applied Sciences, vol 19. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0011-6_9

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  • DOI: https://doi.org/10.1007/978-94-007-0011-6_9

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-0010-9

  • Online ISBN: 978-94-007-0011-6

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