adboost, NN 等分类器模型介绍.ppt

Agenda IntroductionBag of wordsmodelsVisualwordswithspatiallocationPart basedmodelsDiscriminativemethodsSegmentationandrecognitionRecognition basedimageretrievalDatasets Conclusions Classifierbasedmethods Objectdetectionandrecognitionisformulatedasaclassificationproblem Bagofimagepatches andadecisionistakenateachwindowaboutifitcontainsatargetobjectornot Wherearethescreens Theimageispartitionedintoasetofoverlappingwindows Discriminativemethods 106examples Nearestneighbor Neuralnetworks SupportVectorMachinesandKernels ConditionalRandomFields NearestNeighbors 106examples Shakhnarovich Viola Darrell2003 Difficultduetohighintrinsicdimensionalityofimages lotsofdataneeded slowneighborlookup Torralba Fergus Freeman2008 Multi layerHubel Wieselarchitectures Neuralnetworks LeCun Bottou Bengio Haffner1998Rowley Baluja Kanade1998Hinton Salakhutdinov2006Ranzato Huang Boureau LeCun2007 Riesenhuber Poggio1999Serre Wolf Poggio 2005Mutch Lowe2006 Biologicallyinspired SupportVectorMachines Heisele Serre Poggio 2001 Facedetection PyramidMatchKernel CombiningMultipleKernels Varma Roy2007Bosch Munoz Zisserman2007 Grauman Darrell2005Lazebnik Schmid Ponce2006 ConditionalRandomFields Kumar Hebert2003 Quattoni Collins Darrell2004 MoreinSegmentationsection AsimplealgorithmforlearningrobustclassifiersFreund Shapire 1995Friedman Hastie Tibshhirani 1998ProvidesefficientalgorithmforsparsevisualfeatureselectionTieu Viola 2000Viola Jones 2003Easytoimplement notrequiresexternaloptimizationtools Boosting AsimpleobjectdetectorwithBoosting DownloadToolboxformanipulatingdatasetCodeanddatasetMatlabcodeGentleboostingObjectdetectorusingapartbasedmodelDatasetwithcarsandcomputermonitors http people csail mit edu torralba iccv2005 Boosting Boostingfitstheadditivemodel byminimizingtheexponentialloss Trainingsamples Theexponentiallossisadifferentiableupperboundtothemisclassificationerror Weakclassifiers Theinputisasetofweightedtrainingsamples x y w Regressionstumps simplebutcommonlyusedinobjectdetection Fourparameters b Ew y x q a Ew y x q x fm x q Fromimagestofeatures Amyriadofweakdetectors Wewillnowdefineafamilyofvisualfeaturesthatcanbeusedasweakclassifiers weakdetectors Takesimageasinputandtheoutputisbinaryresponse Theoutputisaweakdetector Amyriadofweakdetectors Yuille Snow Nitzbert 1998Amit Geman1998Papageorgiou Poggio 2000Heisele Serre Poggio 2001Agarwal Awan Roth 2004Schneiderman Kanade2004Carmichael Hebert2004 Weakdetectors TexturesoftexturesTieuandViola CVPR2000 Everycombinationofthreefiltersgeneratesadifferentfeature Thisgivesthousandsoffeatures Boostingselectsasparsesubset socomputationsontesttimeareveryefficient Boostingalsoavoidsoverfittingtosomeextend Haarwavelets HaarfiltersandintegralimageViolaandJones ICCV2001 Theaverageintensityintheblockiscomputedwithfoursumsindependentlyoftheblocksize Haarwavelets Papageorgiou Poggio 2000 PolynomialSVM Edgesandchamferdistance Gavrila Philomin ICCV1999 Edgefragments Weakdetector kedgefragmentsandthreshold Chamferdistanceuses8orientationplanes Opelt Pinz Zisserman ECCV2006 Histogramsoforientedgradients Dalal Trigs 2006 ShapecontextBelongie Malik Puzicha NIPS2000 SIFT D Lowe ICCV1999 Weakdetectors Partbased similartopart basedgenerativemodels Wecreateweakdetectorsbyusingpartsandvotingfortheobjectcenterlocation Carmodel Screenmodel Thesefeaturesareusedforthedetectoronthecoursewebsite Weakdetectors Firstwecollectasetofparttemplatesfromasetoftrainingobjects Vidal Naquet Ullman NatureNeuroscience2003 Weakdetectors Wenowdefineafamilyof weakdetectors as Betterthanchance Weakdetectors Wecandoabetterjobusingfilteredimages Stillaweakdetectorbutbetterthanbefore Example screendetection Featureoutput Example screendetection Featureoutput Thresholdedoutput Weak detector Producesmanyfalsealarms Example screendetection Featureoutput Thresholdedoutput Strongclassifieratiteration1 Example screendetection Featureoutput Thresholdedoutput Strongclassifier Secondweak detector Producesadifferentsetoffalsealarms Example screendetection Featureoutput Thresholdedoutput Strongclassifier Strongclassifieratiteration2 Example screendetection Featureoutput Thresholdedoutput Strongclassifier Strongclassifieratiteration10 Example screendetection Featureoutput Thresholdedoutput Strongclassifier Addingfeatures Finalclassification Strongclassifieratiteration200 Wewantthecomplexityofthe3featuresclassifierwiththeperformanceofthe100featuresclassifier Cascadeofclassifiers FleuretandGeman2001 ViolaandJones2001 3features 30features 100features Selectathresholdwithhighrecallforeachstage Weincreaseprecisionusingthecascade Somegoalsforobjectrecognition AbletodetectandrecognizemanyobjectclassesComputationallyefficientAbletodealwithdatastarvingsituations SometrainingsamplesmightbehardertocollectthanothersWewanton linelearningtobefast Sharedfeatures Islearningtheobjectclass1000easierthanlearningthefirst Canwetransferknowledgefromoneobjecttoanother Arethesharedpropertiesinterestingbythemselves Sharedfeatures Screendetector Cardetector Facedetector Independentbinaryclassifiers Torralba Murphy Freeman CVPR2004 PAMI2007 50trainingsamples class29objectclasses2000entriesinthedictionaryResultsaveragedon20runsErrorbars 80 interval Krempp Geman Amit 2002Torralba Murphy Freeman CVPR2004 Sharedfeatures Class specificfeatures Generalizationasafunctionofobjectsimilarities 12viewpoints 12unrelatedobjectclasses Numberoftrainingsamplesperclass Numberoftrainingsamplesperclass AreaunderROC AreaunderRO