Refereed full papers (journals, book chapters, international conferences)

1999

@inproceedings{LuM1999,
	vgclass =	{refpap},
	author =	{L. Lucchese and Sanjit K. Mitra},
	title =	{Unsupervised Segmentation of Color Images Based on
	$k$-means Clustering in the Chromaticity Plane},
	booktitle =	{IEEE Workshop on Content-based Access of Image and Video
	Libraries (CBAIVL'99)},
	address =	{Fort Collins, Colorado, USA},
	pages =	{74--78},
	month =	{June~22},
	year =	{1999},
	abstract =	{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.},
}