Image partitioning with kernel mapping and graph cuts

A novel multiregion graph cut image partitioning method combined with kernel mapping is presented. A kernel function transforms implicitly the image data into data of a higher dimension so that the piecewise constant model of the graph cut formulation becomes applicable. The method yields an effective alternative to complex modeling of the original image data while taking advantage of the rapidity of graph cuts. A variety of noise models are, thus, considered by a single model. Using a common kernel function, we minimize the objective functional by iterating (1) regions parameters update and (2) image partitioning by graph cut iterations. A comparative performance evaluation is carried out over a large set of experiments using synthetic grey level data. Besides, a set of tests with real images such as SAR and medical images is shown to demonstrate the validity of the method.