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

Presents an original technique for unsupervised segmentation of color images which is based on an extension (for use in the u´v´ chromaticity diagram) of the well-known k-means algorithm, which is widely adopted in cluster analysis. We suggest exploiting the separability of color information which, represented in a suitable 3D space, may be “projected” on to a 2D chromatic subspace and on to a 1D luminance subspace. One can first compute the chromaticity coordinates (u´, v´) 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 1D k-means algorithm, which is a simple dimensionally-reduced version of the 2D one. Experimental evidence of the effectiveness of our technique is reported