An efficient unsupervised MRF image clustering method

In this paper, a robust image segmentation method is proposed. The relationship between pixel intensities and distance between pixels are introduced to the traditional neighbourhood potential function To perform an unsupervised segmentation, the Bayes Information Criterion (BIC) is used to determine the class number, the K-means is employed to initialise the classification and calculate the mean values and variances of the classes. The segmentation is transformed to maximize a posteriori(MAP) procedure. Then, the Iterative Conditional Model (ICM) is employed to solve the MAP problem. In the experiments, the proposed method is compared with other segmentation techniques, for noisy image segmentation applying on synthetic and real images. The experiment results shows that the proposed algorithm is the better choice.