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1、ImprovingImageClassificationwithLocationContextKevinTang1,ManoharPaluri2,LiFei-Fei1,RobFergus2,LubomirBourdev21ComputerScienceDepartment,StanfordUniversity2FacebookAIResearchfkdtang,feifeilig@cs.stanford.edufmano,robfergus,lubomirg@fb.comAbstractWiththewidespreada
2、vailabilityofcellphonesandcam-erasthathaveGPScapabilities,itiscommonforimagesbeinguploadedtotheInternettodaytohaveGPScoordi-natesassociatedwiththem.InadditiontoresearchthattriestopredictGPScoordinatesfromvisualfeatures,thisalsoopensupthedoortoproblemsthatarecondi
3、tionedon(a)(b)theavailabilityofGPScoordinates.Inthiswork,wetackletheproblemofperformingimageclassificationwithloca-tioncontext,inwhichwearegiventheGPScoordinatesforimagesinboththetrainandtestphases.Weexplorediffer-entwaysofencodingandextractingfeaturesfromtheGPSco
4、ordinates,andshowhowtonaturallyincorporatethesefeaturesintoaConvolutionalNeuralNetwork(CNN),thecurrentstate-of-the-artformostimageclassificationand(c)(d)recognitionproblems.WealsoshowhowitispossibletosimultaneouslylearntheoptimalpoolingradiiforasubsetFigure1.Which
5、oftheseareimagesofsnow?JustbylookingatofourfeatureswithintheCNNframework.Toevaluateourtheimages,itmaybedifficulttotell.However,whatifweknewmodelandtohelppromoteresearchinthisarea,weidentifythat(a)wastakenattheBonnevilleSaltFlatsinUtah,(b)wasasetoflocation-sensitiv
6、econceptsandannotateasubsetoftakeninNewHamsphire,(c)wastakeninDeathValley,CaliforniatheYahooFlickrCreativeCommons100Mdatasetthathasand(d)wastakennearPaloAlto,California?ImagecreditsgivenGPScoordinateswiththeseconcepts,whichwemakepub-insupplementarymaterial.liclya
7、vailable.Byleveraginglocationcontext,weareableareinterestedinclassifyingconsumerimageswithconceptstoachievealmosta7%gaininmeanaverageprecision.thatcommonlyoccurontheInternet,rangingfromobjectstoscenestospecificlandmarks,asthesearethethingsthatpeopleoftentakepictur
8、esof,andtheInternetisthelargest1.Introductionsourceofgeotaggedimages.BuildingontheCNNarchi-AsFigure1shows,itissometimeshardevenforhumanstectureintroducedin[26]
