International Journal of Computer Theory and Engineering

Abstract

For any learning algorithm, the problems of robustness toward 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. The sensitivity of segmentation solution to image variations is measured by image resampling.A Hierarchial Chamfer matching algorithm implements shape constraints which are included in the inference process to guide ambiguous groupings of color and texture features. Shape and similarity based grouping based information is combined into a semantic likelihood map in the framework of Bayesian statistics.