Multiresolutional cluster segmentation using spatial context

A multiresolutional cluster/relaxation image segmentation algorithm is described. A preliminary split-merge procedure generates variable-sized quadtree-blocks. These multiresolutional units are used in the subsequent clustering. A probabilistic relaxation procedure conducts the final labeling. A large reduction in data processing is attained by processing blocks rather than pixels, while still yielding good segmentation results.