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Dataset Issues in Object Recognition

21 pages
Niveau: Supérieur, Doctorat, Bac+8
Dataset Issues in Object Recognition J. Ponce1,2, T.L. Berg3, M. Everingham4, D.A. Forsyth1, M. Hebert5, S. Lazebnik1, M. Marszalek6, C. Schmid6, B.C. Russell7, A. Torralba7, C.K.I. Williams8, J. Zhang6, and A. Zisserman4 1 University of Illinois at Urbana-Champaign, USA 2 Ecole Normale Superieure, Paris, France 3 University of California at Berkeley, USA 4 Oxford University, UK 5 Carnegie Mellon University, Pittsburgh, USA 6 INRIA Rhone-Alpes, Grenoble, France 7 MIT, Cambridge, USA 8 University of Edinburgh, Edinburgh, UK Abstract. Appropriate datasets are required at all stages of object recognition research, including learning visual models of object and scene categories, detecting and localizing instances of these models in im- ages, and evaluating the performance of recognition algorithms. Current datasets are lacking in several respects, and this paper discusses some of the lessons learned from existing e?orts, as well as innovative ways to obtain very large and diverse annotated datasets. It also suggests a few criteria for gathering future datasets. 1 Introduction Image databases are an essential element of object recognition research. They are required for learning visual object models and for testing the performance of classification, detection, and localization algorithms.

  • multiple algorithms currently

  • per image

  • current datasets

  • fei-fei li

  • datasets avail- able

  • recent recognition

  • recognition algorithms

  • intra-class variability


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DatasetIssuesinObjectRecognition1J.Ponce1,2,T.L.Berg3,M.Everingham4,D.A.Forsyth1,M.Hebert5,S.Lazebnik1,M.Marszalek6,C.Schmid6,B.C.Russell7,A.Torralba7,C.K.I.Williams8,J.Zhang6,andA.Zisserman4UniversityofIllinoisatUrbana-Champaign,USA2EcoleNormaleSup´erieure,Paris,France3UniversityofCaliforniaatBerkeley,USA4OxfordUniversity,UKCarnegieMellonUniversity,Pittsburgh,USAINRIARhoˆne-Alpes,Grenoble,France7MIT,Cambridge,USAUniversityofEdinburgh,Edinburgh,UK586Abstract.Appropriatedatasetsarerequiredatallstagesofobjectrecognitionresearch,includinglearningvisualmodelsofobjectandscenecategories,detectingandlocalizinginstancesofthesemodelsinim-ages,andevaluatingtheperformanceofrecognitionalgorithms.Currentdatasetsarelackinginseveralrespects,andthispaperdiscussessomeofthelessonslearnedfromexistingefforts,aswellasinnovativewaystoobtainverylargeanddiverseannotateddatasets.Italsosuggestsafewcriteriaforgatheringfuturedatasets.1IntroductionImagedatabasesareanessentialelementofobjectrecognitionresearch.Theyarerequiredforlearningvisualobjectmodelsandfortestingtheperformanceofclassification,detection,andlocalizationalgorithms.Infact,publiclyavailableimagecollectionssuchasUIUC[1],Caltech4[10],andCaltech101[9]haveplayedakeyroleintherecentresurgenceofcategory-levelrecognitionresearch,drivingthefieldbyprovidingacommongroundforalgorithmdevelopmentandevaluation.Currentdatasets,however,offerasomewhatlimitedrangeofimagevariability:Althoughtheappearance(andtosomeextent,theshape)ofobjectsdoesindeedvarywithineachclass(e.g.,amongtheairplanes,cars,faces,andmotorbikesofCaltech4),theviewpointsandorientationsofdifferentinstancesineachcategorytendtobesimilar(e.g.,sideviewsofcarstakenbyahorizontalcamerainUIUC);theirsizesandimagepositionsarenormalized(e.g.,theobjectsofinteresttakeupmostoftheimageandareapproximatelycenteredinCaltech101);thereisonlyoneinstanceofanobjectperimage;finally,thereislittleornoocclusionandbackgroundclutter.ThisisillustratedbyFigures1and3fortheCaltech101database,butremainstrueofmostdatasetsavailabletoday.Theproblemswithsuchrestrictionsaretwofold:(i)somealgorithmsmayexploitthem(forexamplenear-globaldescriptorswithnoscaleorrotationin-variancemayperformwellonsuchimages),yetwillfailwhentherestrictions
2Fi.g.1SampleimagesrfmoehtCaltech101dataset,]9[courtesyfo-ieFieFL.i
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