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1、LearningHierarchicalFeatureExtractorsForImageRecognitionbyY-LanBoureauAdissertationsubmittedinpartialfulfillmentoftherequirementsforthedegreeofDoctorofPhilosophyDepartmentofComputerScienceNewYorkUniversitySeptember2012YannLeCunJeanPoncecY-LanBoureauAllRightsReserved,2012DEDICATIONTomyparents.i
2、iiACKNOWLEDGMENTSIamaboveallgratefultoYannLeCunandJeanPonce,forprovidingmepatientandinsightfulguidanceduringmyyearsastheirstudent.Manythanksaswelltotheothermembersofmythesiscommitteeforgivingmefeedbackandideas.IwouldliketothankFrancisBachforbeingsuchagreatinspirationandsharpdiscussant;Ihavebe
3、enveryluckytoworkwithhim,aswellasMarc’AurelioRanzato,NicolasLeRoux,KorayKavukcuoglu,andPierreSermanet,andSamyBengioandJasonWestonatGoogle.ManyideasinthisthesiswerebornwhilediscussingwithmembersoftheWillowandSierrateams,andtheComputationalandBiologicalLearningLab.Finally,Ithankmyfamilyandfrien
4、dsforencouragingmeandbearingwithmeduringalltheseyears.ThisworkwassupportedbyNSFgrantEFRI/COPN-0835878toNYU,ONRcon-tractN00014-09-1-0473toNYUandbytheEuropeanResearchCouncil(VideoWorldandSierragrants).ivABSTRACTTellingcowfromsheepiseffortlessformostanimals,butrequiresmuchengineeringforcomputers
5、.Inthisthesis,weseektoteaseoutbasicprinciplesthatunderliemanyrecentadvancesinimagerecognition.First,werecastmanymethodsintoacommonunsu-pervisedfeatureextractionframeworkbasedonanalternationofcodingsteps,whichencodetheinputbycomparingitwithacollectionofreferencepatterns,andpoolingsteps,whichco
6、mputeanaggregationstatisticsummarizingthecodeswithinsomere-gionofinterestoftheimage.Withinthatframework,weconductextensivecomparativeevaluationsofmanycodingorpoolingoperatorsproposedintheliterature.Ourresultsdemonstratearobustsuperiorityofsparsecoding(whichdecomposesaninputasalinearcombinatio
7、nofafewvisualwords)andmaxpooling(whichsummarizesasetofinputsbytheirmaximumvalue).Wealsoproposemacrofeatures,whichimportintothepopu-larspatialpyramidframeworkthejointencodingofnearbyfeaturescommonlypracticedinneuralnetworks,andobtainsignificant
