TEXTURED IMAGE SEGMENTATION - RETURNING MULTIPLE SOLUTIONS

Traditionally, the goal of image segmentation has been to produce a si ngle partition of an image. This partition is compared to some 'ground truth', or human approved partition, to evaluate the performance of t he algorithm. This paper utilizes a framework for considering a range of possible partitions of the image to compute a probability distribut ion on the space of possible partitions of the image. This is an impor tant distinction from the traditional model of segmentation, and has m any implications in the integration of segmentation and recognition re search. The probabilistic framework that enables us to return a confid ence measure on each result also allows us to discard from considerati on entire classes of results due to their low cumulative probability. The distributions thus returned may be passed to higher-level algorith ms to better enable them to interpret the segmentation results. Severa l experimental results are presented using Markov random fields as tex ture models to generate distributions of segments and segmentations on textured images. Both simple homogeneous images and natural scenes ar e presented. (C) 1997 Elsevier Science B.V.