Color image segmentation using multiscale fuzzy C-means and graph theoretic merging

A multiresolution color image segmentation method is presented that incorporates the main principles of region-based and cluster analysis approaches. A multiscale dissimilarity measure in the feature space is proposed that makes use of nonparametric cluster validity analysis and fuzzy C-Means clustering. Detected clusters are utilized to assign membership functions to the image regions. In addition, a graph theoretic merging algorithm is presented that uses the formulation of fuzzy similarity relations to produce the final segmentation results. The efficiency of the resulting scheme is also experimentally indicated.