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Combining local and global features for image segmentation using iterative classification and region merging

TitleCombining local and global features for image segmentation using iterative classification and region merging
Publication TypeConference Paper
Year of Publication2005
AuthorsYu, Q., and D. A. Clausi
Conference Name2nd Canadian Conference on Computer and Robot Vision
Date Published05/2005
Keywordsedge detection, edge strength, Feature Extraction, global feature, global statistics, image classification, image segmentation, iterative classification, iterative methods, local feature, local statistics, Markov processes, Markov random field, MRF based unsupervised segmentation, MRF energy function, MRF model parameter, region merging
Abstract

In MRF based unsupervised segmentation, the MRF model parameters are typically estimated globally. Those global statistics sometimes are far from accurate for local areas if the image is highly non-stationary, and hence will generate false boundaries. The problem cannot be solved if local statistics are not considered. This work incorporates the local feature of edge strength in the MRF energy function, and segmentation is obtained by reducing the energy function using iterative classification and region merging.