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1、2013IEEEInternationalConferenceonComputerVisionSaliencyDetectionviaAbsorbingMarkovChainBowenJiang1,LiheZhang1,HuchuanLu1,ChuanYang1,andMing-HsuanYang21DalianUniversityofTechnology2UniversityofCaliforniaatMercedAbstractthosesegmentsassaliency,whichcanusuallyhighlighttheentireobject.F
2、ourierspectrumanalysishasalsobeenusedInthispaper,weformulatesaliencydetectionviaab-todetectvisualsaliency[15,13].Recently,Perazzietal.sorbingMarkovchainonanimagegraphmodel.Wejoint-[25]unifythecontrastandsaliencycomputationintoas-lyconsidertheappearancedivergenceandspatialdistri-ingl
3、ehigh-dimensionalGaussianfilteringframework.Weibutionofsalientobjectsandthebackground.Thevirtualetal.[33]exploitbackgroundpriorsandgeodesicdistanceboundarynodesarechosenastheabsorbingnodesinaforsaliencydetection.Yangetal.[35]castsaliencydetec-Markovchainandtheabsorbedtimefromeachtran
4、sienttionintoagraph-basedrankingproblem,whichperformsnodetoboundaryabsorbingnodesiscomputed.Theab-labelpropagationonasparselyconnectedgraphtochar-sorbedtimeoftransientnodemeasuresitsglobalsimilar-acterizetheoveralldifferencesbetweensalientobjectanditywithallabsorbingnodes,andthussal
5、ientobjectscanbackground.beconsistentlyseparatedfromthebackgroundwhentheabsorbedtimeisusedasametric.SincethetimefromInthiswork,wereconsiderthepropertiesofMarkovran-transientnodetoabsorbingnodesreliesontheweightsondomwalksandtheirrelationshipwithsaliencydetection.thepathandtheirspati
6、aldistance,thebackgroundregionExistingrandomwalkbasedmethodsconsistentlyusetheonthecenterofimagemaybesalient.WefurtherexploitequilibriumdistributioninanergodicMarkovchain[9,14]theequilibriumdistributioninanergodicMarkovchaintooritsextensions,e.g.thesiteentropyrate[31]andthereducethe
7、absorbedtimeinthelong-rangesmoothback-hittingtime[11],tocomputesaliency,andhaveachievedgroundregions.Extensiveexperimentsonfourbenchmarksuccessintheirownaspects.However,thesemodelsstil-datasetsdemonstraterobustnessandefficiencyofthepro-lhavesomecertainlimitations.Typically,saliencyme
8、a-posedmethodagains
