Combining Top Down Strategy With Bottom Up Approach For Image Segmentation

Image segmentation is the problem of partitioning an image into its constituent components. In wisely choosing a partition that highlights the role and salient features of each component, we obtain a compact representation of an image in terms of its useful parts. In proposing an integrated approach for image segmentation based on a generative clustering model combined with coarse shape information and robust parameter estimation. The sensitivity of segmentation solution to image variations is measured by image resampling. Top down information & bottom up approach is combined into a semantic likelihood map in the framework of Bayesian statistics.