Sigma filter based unsupervised color image segmentation

In this paper, we present an unsupervised color image segmentation algorithm. 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 differentiate the image into non-overlapping semantic objects. 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 provide examples to illustrate the performance of our procedure.