L. Lucchese and Sanjit K. Mitra,
Unsupervised Segmentation of Color Images Based on
k-means Clustering in the Chromaticity Plane,
In IEEE Workshop on Content-based Access of Image and Video
Libraries (CBAIVL'99),
Fort Collins, Colorado, USA, pp. 74-78, June 22 1999.In this work, we present an original technique for unsupervised segmentation of color images which is based on an extension, for an use in the u prime v prime chromaticity diagram, of the well-known k-means algorithm, widely adopted in cluster analysis. We suggest exploiting the separability of color information which, represented in a suitable 3D space, may be ``projected'' onto a 2D chromatic subspace and onto a 1D luminance subspace. One can first compute the chromaticity coordinates (u prime ,v prime ) of colours and find representative clusters in such a 2D space, by using a 2D k-means algorithm, and then associate these clusters with appropriate luminance values, by using a 1D k-means algorithm, a simple dimensionally reduced version of the previous one. Experimental evidence of the effectiveness of our technique is reported.