Interactive region-based MRF image segmentation

An interactive region-based Markov random field (MRF) image segmentation method is proposed for solving inaccurate parameter estimation and mis-segmentation of MRF method. Because color and texture features in natural image are very complex, unsupervised method cannot accurately achieve segmentation. The proposed method also introduces human-computer interaction to improve segmentation. The segmentation is achieved by classifying pixels into different classes. All these classes can be represented by multivariate Gaussian distributions. In the proposed method, image is firstly separate into homogeneous regions, and interactive information is carried out as manual marks on over segmentation regions to roughly indicate object and background. Feature parameters of object and background can be accurately calculated from marked regions. To solve partial mis-segmentation might appear in MRF model, we use adjacent potential energy as region merging metric to automatically correct mis-segmentation. Empirical results show that the proposed algorithm can accurately segment object from background. Compared with traditional MRF algorithm and unsupervised Graph Cut algorithm, the proposed algorithm achieve better results. Based on more accurate initial parameters and automatic correction of mis-segmentation, the proposed method can well extract object from background.