Classification and tracking using local optimization

Abstract

When a heuristic function is available to evaluate classification, a special search procedure is applied to find a classification optimizing this function. A specific application to image segmentation is presented, including several examples. The major difference between this approach and previous optimization attempts is the use of deterministic rather than probabilistic classifications. The approach is also applied to object tracking in image sequences.