Unsupervised image segmentation using an unlabeled region process

摘要

The segmentation of a gray scale image of regions of uniform or slowly varying intensity is discussed. A Gibbsian representation is given for a partition of a lattice with no labels. The homogeneity of each segment is measured by the sample variance of observations. In a region merging algorithm, the posterior probability of the segmentation is gradually increased. It is shown how to simulate partitions with the Gibbs sampler and how to combine information in simulated segmentations. When the segments cannot be grouped into genuine classes, the segmentation methods are preferable to classification by Iterated Conditional Modes (ICM) or by the Maximizer of Posterior Marginals (MPM).