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一种融合深度基于灰度共生矩阵的感知模型 Title:ACognitiveModelbasedontheFusionofDeepLearningandGrayLevelCo-occurrenceMatrix Abstract: Deeplearningtechniqueshaveachievedremarkablesuccessinavarietyofperceptiontasks,includingimageclassificationandobjectrecognition.However,theseapproachesoftenlackinterpretabilityandexplainability,limitingtheirusefulnessincertainapplications.Ontheotherhand,textureanalysisusingthegraylevelco-occurrencematrix(GLCM)hasbeenwidelyadoptedinvariousdomainsduetoitsrobustnessandinterpretability.Inthispaper,weproposeacognitivemodelthatfusesdeeplearningandGLCMtoleveragetheadvantagesofbothapproaches.Themodelaimstoenhancetheinterpretabilityandgeneralizationperformanceofdeeplearningmodelsfortextureanalysistasks. 1.Introduction Textureanalysisisanessentialstepinmanycomputervisiontasks,suchasmedicalimaging,environmentalmonitoring,andremotesensing.Deeplearningmodels,especiallyConvolutionalNeuralNetworks(CNNs),havedemonstratedtheirsuperiorperformanceintextureclassificationandrecognition.However,theblack-boxnatureofdeeplearningmodelslimitstheirinterpretability,hinderingtheirwidespreadadoption.Toalleviatethisissue,weproposeacognitivemodelthatcombinesdeeplearningwithGLCM,aclassicalandinterpretabletextureanalysismethod. 2.Background 2.1DeepLearning:Thissectionprovidesanoverviewofdeeplearninganditsapplicationsincomputervision.ItexplainstheworkingprinciplesofCNNsandhighlightstheirstrengthsandweaknesses. 2.2GrayLevelCo-occurrenceMatrix(GLCM):ThissectiondescribestheconceptandcomputationprocessofGLCM.ItalsodiscussestheinterpretabilityandrobustnessofGLCMintextureanalysis. 3.ProposedCognitiveModel 3.1ModelArchitecture:WeproposeanovelarchitecturethatcombinesadeepneuralnetworkwithaGLCM-basedmodule.Thedeepneuralnetworkactsasafeatureextractor,capturinghigh-levelrepresentationsfrominputimages.TheGLCMmodulethenincorporatestextureinformationintothedeepfeatures,enhancingtheinterpretabilityofthemodel. 3.2TrainingProcedure:Wedescribethetrainingprocessoftheproposedcognitivemodel,includingdataprepro