Fuzzy Approaches for Colour Image Palette Selection

  • Gerald Schaefer
  • Huiyu Zhou
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 58)

Abstract

Colour quantisation algorithms are used to display true colour images using a limited palette of distinct colours. The choice of a good colour palette is crucial as it directly determines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best represent the colours in an image. In this paper we investigate the performance of various fuzzy c-means clustering algorithms for colour quantisation of images. In particular, we use conventional fuzzy c-means as well as some more efficient variants thereof, namely fast fuzzy c-means with random sampling, fast generalised fuzzy c-means, and anisotropic mean shift based fuzzy c-means algorithm. Experimental results show that fuzzy c-means performs significantly better than other, purpose built colour quantisation algorithms, and also confirm that the fast fuzzy clustering algorithms provide quantisation results similar to the full conventional fuzzy c-means approach.

Keywords

Cluster Centre Colour Palette Bandwidth Matrix Anisotropic Kernel True Colour Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gerald Schaefer
    • 1
  • Huiyu Zhou
    • 2
  1. 1.School of Engineering and Applied ScienceAston UniversityBirminghamU.K.
  2. 2.School of Engineering and DesignBrunel UniversityUxbridgeU.K.

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