Texture classification by multi-model feature integration using Bayesian networks

Inthis paper, a textureclasehSVNUSh methodbaso on multi-model feature integration byBayesNE networks is proposks Consks.E that many imagetextures exhibit bothshhz@UAAh andshEVNAzh.E properties two featuresat bast on two texturemodels ----the Gabor model and theGaus#N@ Markov random field model are ush todesV(zU the image properties in bothsthhSE#U andshS@@UNh.E ABayes#h networkclasrkhE is then usn to combinethes two soh of features along with their individual confidencemeasenc for texture clasehEAzzAh. Seventy eight Brodatztextures were usr to evaluate theclas(E(h.zU( performance. ThereszVz ss that theproposN methodis better than that usth a shV(N sh offeatures from either model for texture clasehzE@USh