Natural color image segmentation
Jie Xu, Shi Peng-fei · 2004
A new method for natural color image segmentation using integrated features is proposed in this paper. Edges are first detected in term of the high phase congruency in the gray-level image. K-means cluster is used to label long edge lines based on the global color information to estimate roughly the distribution of objects in the image, while short ones are merged based on their positions and local color differences to eliminate the negative affection caused by texture or other trivial features in image. Region growing technique is employed to achieve the final segmentation results. The proposed method unifies edges, both the whole and local color distributions, as well as the spatial information to solve the natural image segmentation problem. The feasibility and effectiveness of this method have been demonstrated by various experiments.