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Unsupervised image segmentationby Global and local Criteria Optimization Based on Bayesian Networks
Today Bayesian networks are moreusedinmanyareas ofdecision support and image processing. In this way, our proposed approach uses Bayesian Network to modelize the segmented image quality.This quality iscalculated ona set ofattributesthat representlocalevaluation measures.Theidea is to havethese local levelschosenin a wayto beintersected intothemto keep theoverall appearance ofsegmentation.The approachoperates in twophases: the first phaseis to make an over-segmentationwhich gives superpixels card. In the second phase, we model thesuperpixelsbya Bayesian Network.To findthe segmented imagewith the bestoverall qualitywe used twoapproximateinference methods, the first using ICM algorithm which iswidely used inMarkov Models anda second is a recursive methodcalledalgorithmof model decomposition based on max-product algorithm which is very popular in therecent worksof image segmentation.For our model,we have shown thatthecompositionof these two algorithmsleads togoodsegmentationperformance.
Image segmentation, Probabilistic segmentation, Bayesian Network, Superpixel, ICM, Max-product algorithms.
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