Probabilistic rules for automatic texture segmentation
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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We propose a general unsupervised multi-scale feature-based approach towards image segmentation. Clusters in the feature space are assumed to be properties of underlying classes, the recovery of which is achieved by the use of the mean shift procedure, a robust non-parametric decomposition method. The subsequent classification procedure consists of Bayesian multi-scale processing which models the inherent uncertainty in the joint class and position domains via a Multi-scale Random Field model. At every scale, the segmentation map and model parameters are estimated by sampling using Markov Chain Monte Carlo simulations. The method is applied to perform color and texture segmentation with good results.