Image segmentation as regularized clustering: a fully global curve evolution method

The purpose of this study is to investigate image segmentation from the viewpoint of image data regularized clustering. From this viewpoint, segmentation into a fixed but arbitrary number N of regions is stated as the simultaneous minimization of N - 1 energy functional, each involving a single region and its complement. The resulting Euler-Lagrange curve evolution equations yield a partition at convergence provided the curves are initialized so as to define an arbitrary partition of the image domain. The method is implemented via level sets, and results are shown on synthetic and natural vectorial images.