Fuzzy sets theory based region merging for robust image segmentation

Authors:
Hongwei Zhu;Otman Basir
Affiliations:
Pattern Analysis and Machine Intelligence Research Group, University of Waterloo, Waterloo, Ontario, Canada;Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Year:
2005

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Abstract

A fuzzy set theory based region merging approach is presented to tackle the issue of oversegmentation from the watershed algorithm, for achieving robust image segmentation. A novel hybrid similarity measure is proposed as the merging criterion, based on the region-based similarity and the edge-based similarity. Both similarities are obtained using the fuzzy set theory. To adaptively adjust the influential degree of each similarity to region merging, a simple but effective weighting scheme is employed with the weight varying as region merging proceeds. The proposed approach has been applied to various images, including gray-scale images and color images. Experimental results have demonstrated that the proposed approach produces quite robust segmentations.