Color image segmentation based on edge-preservation smoothing and soft C-means clustering

A new approach to color image segmentation is demonstrated here. The color image, which is usually in the RGB space, is translated into the CIE(Lab) color space. The three components are smoothed using a variation-based approach. By minimizing an energy functional with a nonconvex regular function, we can get a smoothed image. During the iteration, the edges of the image are preserved. A soft C-means clustering algorithm, which is an improvement on the hard C-means algorithm, is employed to segment them after smoothing. This algorithm overcomes the problem of dependence on the initializations. Finally, an experiment is given to show the effectiveness and robustness of the method.