Unsupervised-Learning-of-Categories-from-Sets-of-Partially-無(wú)監(jiān)督學(xué)習(xí)的類(lèi)別從套部分課件

GoalAutomaticallyrecovercategoriesfromanunlabeledcollectionofimages,andformpredictiveclassifierstolabelnewimages

Tolerateclutter,occlusion,commontransformationsAllowoptional,variableamountofsupervisionEfficiencyMotivationMajorityoftheobjectrecognitionandcategorizationrequires‘ManualAnnotations’.‘ManualAnnotations’:practicallimitonnumberofclassesandnumberoftrainingexamplesperclass.IntroducesBiasing:effectstheperformance.Unsupervisedrecognitionandcategorizationwouldbecosteffectiveandreduceburden.MotivationMajorityoftheobjectrecognitionandcategorizationrequires‘ManualAnnotations’.‘ManualAnnotations’:

practicallimitonnumberofclassesandnumberoftrainingexamplesperclass.IntroducesBiasing:effectstheperformance.Unsupervisedrecognitionandcategorizationwouldbecosteffectiveandreduceburden.RelatedworkModelingsceneswithlocaldescriptorsICCV2019,P.Quelhas,F.Monay,etc.DiscoveringObjectCategoriesinImageCollectionsICCV2019,J.Sivic,B.Russell,A.Efros,etc.LearningObjectCategoriesfromGoogle’sImageSearchICCV2019,R.Fergus,L.Fei-Fei,etc.Relatedwork(contd.)Localfeatures–recognitionandretrievalUnorderedfeaturevectorsandVaryinsizePreviousApproaches:vectorquantizationBuildcodebookoffeaturedescriptorsApplyconventionalclusteringmethodsorlatentsemanticanalysis(LSA)Issues:CluttersOcclusionsAdditionofsmallamountoflabeleddata.ApproachPairwiseaffinitiesreflectingpartial-matchfeaturecorrespondencesarecomputedforallinputimages.(Optional)Avariableamountofsupervisedlabels(pairingconstraints)areoptionallycollectedfromtheuserandaffinitymatrixareadjustedaccordingly.Spectralclusteringisusedtorecovertheinitialdominantclusters.Prototypicalexamples:byevaluatingthetypical“featuremasks”contributingtoeachwithin-clusterpartialmatchingTop-rankedprototypicalexamplesfromrefinedgroupingscomposethelearnedcategories,whichareusedtotrainasetofpredictiveclassifiersforlabellingunseenexamples.PyramidmatchkernelNumberofnewlymatchedpairsatleveliMeasureofdifficultyofamatchatleveliApproximatepartialmatchsimilaritySetsoffeaturesFeatureextraction,Histogrampyramid:levelihasbinsofsize2i

PyramidmatchkernelWeightsinverselyproportionaltobinsizeNormalizekernelvaluestoavoidfavoringlargesetsmeasureofdifficultyofamatchatlevelihistogrampyramidsnumberofnewlymatchedpairsatleveliPyramidmatchkernelAlgorithmInitialGroupingFeatureSetswithpartialcorrespondences.InferringCategoryFeatureMasks.IdentifyingPrototypes.InitialGroupingDataSetU={I1,I2,…,IN}whereIiareimages.IiisdenotedbyfeaturesetXi={f1,…,fmi}wheremimayvaryacrossUfi:descriptionvectors.AuthorsusesSHIFTforlocalfeature.InitialGrouping(contd.)DerivedPartialCorrespondenceusingPyramidMatchKernel.InitialGrouping(contd.)Normalizedcutscriteriontocreatepreliminarilyclasses.OptionallyIntroduceofweaksemisupervisionintheformofpairwiseconstraintsbetweentheunlabeledimagesAdjusttheweightofaffinitymatrixtohighestvaluefor“mustgroup”andzeroto“cannot–group”.InitialGrouping(contd.)BackgroundfeaturematchesMultipleobjectmatchesInferringCategoryFeatureMasksConfoundingnormalizedcuts.Goal:Identifyprototypicalexample.Intuition:Inlierusessimilarpartialportionoffeaturesetstoformpartialmatches.UseModifiedpyramidKernel.IdentifyingPrototypesUsethefeaturemasktoreweighttheindividualfeature.Sorttheimagesbasedonhowconsistenttheymatchwiththeremainderofsets.Exampleswithintopthresholdpercentileare“prototypecandidates”O(jiān)ptionallyallowuserconstraintsandadjustaffinitymatrixincaseofdisagreementInferringfeaturemasksfeatureindexcontributiontomatchInferringfeaturemasksweightedfeaturemaskRefiningintra-clustermatchesweightedfeaturemaskRefiningintra-clustermatchesweightedfeaturemaskRefiningintra-clustermatchesweightedfeaturemaskRefiningintra-clustermatchesweightedfeaturemaskRefiningintra-clustermatchesweightedfeaturemaskSelectingcategoryprototypesSelectingcategoryprototypesUnsupervisedrecoveryofcategoryprototypesToppercentileofprototypesPrototypeaccuracy/categoryCaltech-4datasetSemi-supervisedcategorylabelingCaltech-4datasetRecognitionrate/classAmountofsupervisoryinformation(numberof“must-group”pairs)FutureWorkNumberofgroupingsareneededtospecifiedduringnormalizedcuts.(limitations)Findingofmostcriticallocalfeaturesandinterestpoints.(performance)Usingthealgorithmforimageretrival.References

ShapeMatchingandObjectRecognitionUsingShapeContextsS.Belongie,J.Malik,andJ.Puzicha,IEEETranc.PAMI2019ShapeMatchingandObjectRecognitionusingLowDistortionCorrespondencesA.Berg,T.Berg,andJ.Malik,IEEECVPR2019DistinctiveImageFeaturesfromScaleInvariantKeypointsDLowe,InternationalJournalofComputerVision2019IndexingBasedonScaleInvariantInterestPoints K.MikolajczykandC.Sch