一种新的基于Gabor小波的非监督纹理分割方法-豆柴文库

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一种新的基于Gabor小波的非监督纹理分割方法
Title:ANovelUnsupervisedTextureSegmentationMethodBasedonGaborWavelets
Abstract:
Texturesegmentationplaysavitalroleinvariouscomputervisionapplications.ThispaperpresentsanovelunsupervisedtexturesegmentationmethodbasedonGaborwavelets.TheGaborwavelettransformiswidelyusedfortextureanalysisduetoitsabilitytocapturebothspatialandfrequencyinformation.Ourproposedmethodcombinesmulti-channelGaborfeatures,aself-organizingmap(SOM),andafuzzyC-meansclusteringalgorithmtoachieveefficientandaccuratetexturesegmentation.Experimentalresultsdemonstratetheeffectivenessandrobustnessoftheproposedmethod.
Keywords:Texturesegmentation,Gaborwavelets,unsupervisedlearning,self-organizingmap,fuzzyC-means
1.Introduction
Textureisanessentialpropertyofimagesthatcontainsrepetitivepatternsorstructures,whichareoftenchallengingtosegmentaccurately.Texturesegmentationaimstodivideanimageintodistinctregionsbasedonthesepatternsorstructures.Itplaysacrucialroleinvariouscomputervisiontasks,includingobjectrecognition,textureclassification,andimageretrieval.Supervisedtexturesegmentationmethodsusuallyrequirealargeamountofmanuallylabeledtrainingdata,whichcanbetime-consumingandlabor-intensive.Inthispaper,weproposeanovelunsupervisedtexturesegmentationmethodthatleveragesGaborwaveletstoovercometheselimitations.
2.GaborWaveletsforTextureAnalysis
Gaborwaveletsarewidelyusedintextureanalysisduetotheirabilitytocapturebothspatialandfrequencyinformationsimultaneously.TheGaborwavelettransformdecomposesanimageintodifferentfrequencysub-bandsandextractslocalorientationinformation.TheresultingGaborfeaturesaresensitivetolocaltexturepropertiesandareeffectiveincapturingtexturedetails.ByapplyingGaborwaveletstotexturesegmentation,wecanbettercapturethetexturepatternsandstructuresofanimage.
3.ProposedMethod
Ourproposedmethodconsistsofthreestages:featureextraction,self-organizingmap(SOM)training,andfuzzyC-meansclustering.
3.1FeatureExtraction
Inthisstage,wecomputemulti-channelGaborfeaturesfromtheinputimage.TheGaborfi