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.
Index Terms—Image segmentation, Expectation Maximization algorithm, Clustering, resampling, Bayesian statistics.
Mamta S. Kalas is doing M. TECH (CST) at Shivaji University, Kolhapur of Maharashtra.
Cite: Mamata S. Kalas, "Shape Based Image Segmentation Using Bootstrap Resampling and Hcma.," International Journal of Computer Theory and Engineering vol. 1, no. 4, pp. 413-419, 2009.