Textured image segmentation as a multiple hypothesis test

A coarse segmentation algorithm is presented for segmenting textured images which are composed of regions in each of which the data are modeled as one of C Markov random fields (MRFs). The segmentation sought is a maximum-likelihood (ML) segmentation. The image is partitioned into relatively small disjoint square windows. Each window is examined to see whether it is homogeneous or is mixed, and the texture region(s) that comprises the window is (are) decided by a multiple hypothesis test. The formulation of the complex ML segmentation problem in terms of this simpler window-based multiple-hypothesis problem provides huge computational savings, as ML segmentation is only performed at the windows that fall on the boundary between two regions and with the full knowledge of the two populations that are present in the window. Although the problems and solutions presented are for textured image segmentation, they are extendable to problems such as system identification, speech recognition, and data fusion