Unsupervised Segmentation of Textured Images Using Binomial Markov Random Fields
نویسندگان
چکیده
We show how scanned images can be segmented on the basis of their stochastic nature without a priori knowledge of the number of regions or their model parameters. The region distribution is modelled by a Mesh Markov Random Field while each region is filled with textures modelled by a Binomial Markov Random Field (BMRF). A hierarchical fuzzy clustering method is used to estimate the BMRF parameters. Image regions are initially segmented at a coarse level by classifying blocks of pixels according to their joint probabilities, using a maximum local-likelihood estimator based on the clustered parameters. Then these are refined by the simulated annealing method to maximise the a posteriori (MAP) estimation of the region labelling. After briefly describing BMRF models the method is outlined and three demonstration results are given for a visual comparison. One of these is a simulated image, the other two are built from scanned images of Brodatz textures.
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