Color image segmentation using multi-scale clustering

Abstract

The use of clustering in color image segmentation poses two distinct problems: (a) equal distances throughout a color space may not be perceived equally by the human visual system, and (b) the number of color clusters must be predetermined. This paper describes a color clustering method that resolves these problems. The first problem is addressed by operating in the nonlinear, geodesic chromaticity space where color shifts are nearly uniform. The second problem is remedied by utilizing a newly developed multi-scale clustering algorithm. This algorithm determines the prominent numbers of color clusters via an objective measure named lifetime. The obtained segmentation results indicate that this color segmentation approach identifies the prominent color structures or objects in a color image.