Segmentation of Distinct Homogeneous Color Regions in Images

Abstract. In this paper, we present a novel algorithm to detect homogeneous color regions in images. We show its performance by applying it to skin detection. In contrast to previously presented methods, we use only a rough skin direction vector instead of a static skin model as a priori knowledge. Thus, higher robustness is achieved in images captured under unconstrained conditions. We formulate the segmentation as a clustering problem in color space. A homogeneous color region in image space is modeled using a 3D gaussian distribution. Parameters of the gaussians are estimated using the EM algorithm with spatial constraints. We transform the image by a whitening transform and then apply a fuzzy k-means algorithm to the hue value in order to obtain initialization parameters for the EM algorithm. A divisive hierarchical approach is used to determine the number of clusters. The stopping criterion for further subdivision is based on the edge image. For evaluation, the proposed method is ...

• Type: Conference
• Year: 2007
• Where: CAIP
• Authors: Daniel Mohr, Gabriel Zachmann
• Keywords: CAIP 2007, EM Algorithm, Homogeneous Color Region, Rough Skin Direction