Unsupervised segmentation of color images based on k-means clustering in the chromaticity plane

lit this work, we present art original technique for unsupervised segmentation of color images which is based on an extension, for art use in the mu'upsilon' chromaticity diagram, of the well-known k-means algorithm, widely adopted in cluster analysis. We suggest exploiting the separability of color information which, represented ina suitable 3D space, may be "projected" onto a 2D chromatic subspace and onto a ID luminance subspace. One can first compute the chromaticity coordinates (mu',upsilon') of colors 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 ID k-means algorithm, a simple dimensionally reduced version of the previous one. Experimental evidence of the effectiveness of our technique is reported.