Rough Image Colour Quantisation

Authors:
Gerald Schaefer;Huiyu Zhou;Qinghua Hu;Aboul Ella Hassanien
Affiliations:
Department of Computer Science, Loughborough University, Loughborough, U.K.;Queen's University Belfast, Belfast, U.K.;Harbin Institute of Technology, China;Information Technology Department, Cairo University, Giza, Egypt
Venue:
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

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Abstract

Colour quantisation algorithms are essential for displaying 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 use a rough c-means clustering algorithm for colour quantisation of images. Experimental results on a standard set of images show that this rough image quantisation approach performs significantly better than other, purpose built colour quantisation algorithms.