Journal Homepage: www.ijcst.org

Abstract – Image segmentation is an important processing step in many image, video and computer vision applications. Automated segmentation of images has been considered an important intermediate processing task to extract semantic meaning from pixels. The sensitivity of segmentation solutions to image variations is measured by image re-sampling. For any learning algorithm, the problems of robustness towards small fluctuations in the data as well as the generalization of inferred solution to previous unseen instances of dataset from the chosen domain are highly relevant. Image segmentation as a learning problem requires inferring a robust partitioning of image patches with generalization to novel images of the same type. Shape information is included in the inference process to guide ambiguous groupings of colour and texture features. Shape and similarity-based grouping information is combined into a semantic likelihood map in the framework of Bayesian statistics