Multiwavelets domain singular value features for image texture classification-中国期刊网

摘要 Anewapproachbasedonmultiwaveletstransformationandsingularvaluedecomposition(SVD)isproposedfortheclassificationofimagetextures.LowersingularvaluesaretruncatedbasedonitsenergydistributiontoclassifythetexturesinthepresenceofadditivewhiteGaussiannoise(AWGN).Theproposedapproachextractsfeaturessuchasenergy,entropy,localhomogeneityandmax-minratiofromtheselectedsingularvaluesofmultiwaveletstransformationcoefficientsofimagetextures.Theclassificationwascarriedoutusingprobabilisticneuralnetwork(PNN).Performanceoftheproposedapproachwascomparedwithconventionalwaveletdomaingraylevelco-occurrencematrix(GLCM)basedfeatures,discretemultiwaveletstransformationenergybasedapproach,andHMMbasedapproach.Experimentalresultsshowedthesuperiorityoftheproposedalgorithmswhencomparedwithexistingalgorithms.

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Multiwavelets domain singular value features for image texture classification

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Multiwavelets domain singular value features for image texture classification

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