为什麼image无法处理的像text一样好.ppt

Content BasedImageRetrieval ApproachesandTrendsoftheNewAge RitendraDatta JiaLi andJamesZ WangThePennsylvaniaStateUniversityMIR2005 INTRODUCTION 為什麼image無法處理的像text一樣好Textisman screation imagesareamerereplicaofwhatmanhasseenInterpretationofwhatweseeishardtocharacterizevisualsimilarity semanticsimilarityCBIRhasgrowntremendouslyafter2000 notjustintermsofsize butalsointhenumberofnewdirectionsexplored INTRODUCTION INTRODUCTION ThetheoreticalfoundationbehindhowwehumansinterpretimagesisstillanopenproblemAbriefscanningofabout300relevantpaperspublishedinthelastfiveyearsrevealedthatlessthan20 wereconcernedwithapplicationsorreal worldsystems CBIR領域研究方向 FeatureExtractionApproachestoRetrievalAnnotationandConceptDetectionRelevanceFeedbackandLearningHardwareandInterfaceSupport FeatureExtraction 如何抽ColorFeature AnEfficientColorRepresentationforImageRetrieval 比傳統histograms好 MultiresolutionHistogramsandTheirUseforRecognition 用在texturedimage ImageretrievalusingcolorhistogramsgeneratedbyGaussmixturevectorquantization 利用GMVQ抽colorhistogram FeatureExtraction Color Texture抽取 Wavelet BasedTextureRetrievalUsingGeneralizedGaussianDensityandKullback LeiblerDistance Shape ShapeMatchingandObjectRecognitionUsingShapeContexts isfairlycompactyetrobusttoanumberofgeometrictransformations FeatureExtraction Segmentation NormalizedCutsandImageSegmentation 最重要的方向之一 Blobworld ImageSegmentationUsingExpectation maximizationandItsApplicationtoImageQuerying 我之前用過的方法 SegmentationofBrainMRImagesThroughaHiddenMarkovRandomFieldModelandtheExpectation MaximizationAlgorithm 處理medicalimaging FeatureExtraction 線條相似度 Imageretrievalusingwavelet basedsalientpoints 如何選擇featureApplication specificfeaturesets 最直觀的 SIMPLIcity Semantics SensitiveIntegratedMatchingforPictureLibraries semantics sensitivefeatureselection FeatureSelectionforSVMs 用classifier ApproachestoRetrieval Regionbasedimageretrieval AScalableIntegratedRegion BasedImageRetrievalSystem region basedquerying BlobWorld Vectorquantization VQ onimageblocks Keyblock AnApproachforContent basedImageRetrieval generatecodebooksforrepresentationandretrieval takinginspirationfromdatacompressionandtext basedstrategies ApproachestoRetrieval Windowedsearch Object BasedImageRetrievalUsingtheStatisticalStructureofImages moreeffectivethanmethodsbasedoninaccuratesegmentation Anchoring basedimageretrieval AStudyofImageRetrievalbyAnchoring Anchoringisbasedontheideaoffindingasetofrepresentative anchor imagesanddecidingsemanticproximitybetweenanarbitraryimagepairintermsoftheirsimilaritytotheseanchors ApproachestoRetrieval Probabilisticframeworksforimageretrieval AProbabilisticArchitectureforContent basedImageRetrieval AnnotationandConceptDetection Supervisedclassification ImageClassificationforContent BasedIndexing involvingsimpleconceptssuchascity landscape sunset andforest havebeenachievedwithhighaccuracy Translationapproach Objectrecognitionasmachinetranslation Learningalexiconforafixedimagevocabulary 我們在clef2004就是follow這方法 AnnotationandConceptDetection 為何如此困難Wehumanssegmentobjectsbetterthanmachines havinglearnedtoassociateoveralongperiodoftime throughmultipleviewpoints andliterallythrougha streamingvideo atalltimesTheassociationofwordsandblobsbecometrulymeaningfulonlywhenblobsisolateobjectswell RelevanceFeedbackandLearning RelevanceFeedbackinImageRetrieval AComprehensiveReview ProblemsOneproblemwithRFisthataftereveryroundofuserinteraction usuallythetopresultswithrespecttothequeryhavetoberecomputedAnotherissueistheuser spatienceinsupportingmulti roundfeedbacks REAL WORLDREQUIREMENTS PerformanceSemanticlearningVolumeofDataConcurrentUsageHeterogeneityMulti modalfeaturesUser interfaceOperatingSpeedSystemEvaluation CURRENTRESEARCHTRENDS JournalsIEEET PatternAnalysisandMachineIntelligence PAMI IEEET ImageProcessing TIP IEEET CircuitsandSystemsforVideoTechnology CSVT IEEET Multimedia TOM J MachineLearningResearch JMLR InternationalJ ComputerVision IJCV CURRENTRESEARCHTRENDS PatternRecognitionLetters PRL ACMComputingSurveys SURV ConferencesIEEEComputerVisionandPatternRecognition CVPR InternationalConferenceonComputerVision ICCV EuropeanConferenceonComputerVision ECCV IEEEInternationalConferenceonImageProcessing ICIP CURRENTRESEARCHTRENDS ACMMultimedia MM ACMSIGInformationRetrieval IR ACMHumanFactorsinComputingSystems CHI CONCLUSIONS Weha