The Monte Carlo Algorithm for Image Segmentation Based on the MRF Model

Image segmentation is a key technique in the image processing and a classic problem. A Monte Carlo segmentation algorithm based on the Markov Random Field (MRF) image model is proposed to randomly initialize the model parameters, so as to avoid the over-dependence of the algorithm on the initial value and overcome the shortcomings of the local optimal solution of the existing iterative algorithm. Firstly, the MRF model can make full use of the neighborhood relationship of pixel space to obtain the data field information of the image. Then according to the Bayesian theory, the prior knowledge of images is transformed into the prior distribution model. Finally, the Monte Carlo segmentation algorithm is used to iterate until the maximum posterior probability is reached, thus, the distribution of image labels is obtained, that is, the process of image segmentation is completed. The segmentation experiment shows that the Monte Carlo algorithm can overcome the shortcoming of the traditional iterative algorithm, which is trapped in the local optimal, and can segment the image in a more complete and detailed way, effectively realize the accuracy of segmentation, and improve the speed of image segmentation.