Probabilistic Model Based Image Segmentation

There exists a plethora of algorithms to perform image segmentation and there are several issues related to execution time of these algorithms. Image Segmentation is nothing but label relabeling problem under probability framework. To estimate the label configuration, an iterative optimization scheme is implemented to alternately carry out the maximum a posteriori (MAP) estimation and the maximum likelihood (ML) estimations. In this paper this technique is modified in such a way so that it performs segmentation within stipulated time period. The extensive experiments shows that the results obtained are comparable with existing algorithms. This algorithm performs faster execution than the existing algorithm to give automatic segmentation without any human intervention. Its result match image edges very closer to human perception.