Fuzzy clustering for colour reduction in images

Gerald Schaefer*, Huiyu Zhou

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    The aim of colour quantisation is to reduce the number of distinct colour in images while preserving a high colour fidelity as compared to the original images. 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 a recently introduced 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 similar quantisation results to the full conventional fuzzy c-means approach.

    Original languageEnglish
    Pages (from-to)17-25
    Number of pages9
    JournalTelecommunication Systems
    Issue number1-2
    Early online date11 Oct 2008
    Publication statusPublished - Feb 2009


    • Colour palette
    • Colour quantisation
    • Fuzzy c-means
    • Fuzzy clustering


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