Robust natural image segmentation by using spatially constrained multivariate mixed Student’s t-distribution and TV flow edge

• A new boundary extracted method of total variation flow edge (TVFE) is proposed.
• The spatially constrained Markov random field (MRF) is integrated with graph cut.
• The Expectation Maximum and Multilayer graph cut (EM2GC) algorithm is presented.

In this paper, a new method of spatially constrained energy function and total variation flow edge (TVFE) model is proposed for natural image segmentation. As the segmentation process of natural image is sensitive to corrupted noise, the Markov random field (MRF) based spatially constrained information is combined with Graph cut model. Secondly, a total variation flow edge is extracted by calculating the accumulated gradient difference from a nonlinear diffusion filter image. Thirdly, to improve the segmentation performance, the spatially constrained region term and the TVFE edge term are combined together. As the optimization solution of the proposed model is NP hard problem, and then an Expectation Maximum and Multilayer graph cut (EM2GC) algorithm is presented. Lastly, testing experiments are carried out on some synthetic images and real natural scene images, which demonstrate the superiority of our proposed method, such as the effectiveness, robustness, and high accuracy.