Combining Local and Global Features for Image Segmentation Using Iterative Classification and Region Merging

Authors:
Qiyao Yu;David A. Clausi
Affiliations:
University of Waterloo, Canada;University of Waterloo, Canada
Venue:
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Year:
2005

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Unsupervised multi-class segmentation of SAR images using triplet Markov fields models based on edge penalty

Pattern Recognition Letters

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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.