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1、图像分类中的分割模型AimGivenanimageandobjectcategory,tosegmenttheobjectSegmentationshould(ideally)beshapedliketheobjecte.g.cow-likeobtainedefficientlyinanunsupervisedmannerabletohandleself-occlusionSegmentationObjectCategoryModelCowImageSegmentedCowInthissection:briefpaperreviewsJigsawapproach:
2、Borenstein&Ullman,2001,2002Concurrentrecognitionandsegmentation:YuandShi,2002Imageparsing:Tuetal.2003Interleavedsegmentation:Liebe&Schiele,2004,2005OBJCUT:Kumaretal.2005LOCUS:WinnandJojic,2005Object-SpecificFigure-GroundSegregationSomesegmentation/detectionresultsYuandShi,2002Imagepar
3、sing:Tu,ZhuandYuille2003Imageparsing:Tu,ZhuandYuille2003ImplicitShapeModel-RecognitionBackprojected HypothesesInterestPointsMatchedCodebook EntriesProbabilistic VotingVotingSpace (continuous)Backprojection ofMaximaSegmentationRefinedHypotheses (uniformsampling)LiebeandSchiele,2003,200
4、5Interpretationofp(figure)mapper-pixelconfidenceinobjecthypothesisUseforhypothesisverificationp(figure)p(ground)SegmentationSegmentationp(figure)p(ground)OriginalimageLiebeandSchiele,2003,2005Cows:ResultsSegmentationsfrominterestpointsSingle-framerecognition-Notemporalcontinuityused!L
5、iebeandSchiele,2003,2005OBJCUT: shapeprior--LayeredPictorialStructures(LPS)GenerativemodelCompositionofparts+spatiallayoutLayer2Layer1PartsinLayer2canoccludepartsinLayer1SpatialLayout(PairwiseConfiguration)Kumar,etal.2004,2005OBJCUTProbabilityoflabellinginadditionhasUnarypotentialwhic
6、hdependondistancefromΘ(shapeparameter)D(pixels)m(labels)Θ(shapeparameter)ImagePlaneObjectCategorySpecificMRFxymxmyUnaryPotentialΦx(mx
7、Θ)Kumar,etal.2004,2005IntheabsenceofaclearboundarybetweenobjectandbackgroundSegmentationImageOBJCUT:ResultsUsingLPSModelforCowLOCUSmodelDeformationfiel
8、dDPosition&sizeTClassshapeπClassedgespriteμo,σoEdgeimageeImageObjectappearanceλ1Backgroundappearanceλ0MaskmSharedbetweenimagesDifferentforeachimageWinnandJojic,2005SummaryStrengthExplainseverypixeloftheimageUsefulforimageediting,layering,etc.IssuesInvarianceissues(especially)scale,vie
9、w-poi
