Affiliations: Department of Computer Science, Loughborough University, U.K., [email protected] | Institute of Advanced Power, Control and Reliability, Harbin Institute of Technology, China | Institute of Electronics, Communications and Information Technology, Queens University Belfast, U.K. | Department of Electrical and Computer Engineering, University of Manitoba, Canada | Information Technology Department, Cairo University, Egypt
Note: [] Address for correspondence: Department of Computer Science, Loughborough University, Loughborough, LE11 3TU, U.K
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 propose rough c-means and fuzzy rough c-means clustering algorithms for colour quantisation of images. Both approaches utilise the concept of lower and upper approximations of clusters to define palette colours. While in the rough c-means approach cluster centroids are refined iteratively through a linear combination of elements of the lower and upper approximations, the fuzzy rough c-means technique assigns variable membership values to the elements in the boundary region which in turn are incorporated into the calculation of cluster centres. Experimental results on a standard set of images show that these approaches performs significantly better than other, purpose built colour quantisation algorithms.
