Unsupervised color image segmentation for content based application
We present an efficient unsupervised color image segmentation algorithm by combining the local and global color information. By first processing a color image via the proposed color sigma filter, pixels within the same semantic region become more concentrated around their centroid in the perceptual color coordinate system. A k-mean algorithm is then designed to automatically distinguish the image into non-overlapping semantic regions, of which centroids with similar color features are merged automatically. Because of the periodicity in the hue component, we apply two manifolds to completely cover the hue vector, and fuse distinguished regions from both manifolds to obtain the final image segmentation. The computational complexity of our algorithm is O(N), where N is the total number of pixels, and no priori information is assumed. We download sample images from the Internet randomly and apply the proposed algorithm to illustrate the performance of our procedure.