We propose a new algorithm, based on a tree-structured Markov random field (MRP) model, to carry out the unsupervised classification of images. It presents several appealing features; due to the MRF model, it takes into account spatial dependencies, yet is computationally light because only binary MRFs are used and a progressive refinement of information takes place. Moreover, it is adaptive to the local characteristics of the image and provides useful side information about the segmentation process.
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Poggi et al. (1999) studied this question.