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1、FastContourMatchingUsingApproximateEarthMover’sDistanceKristenGraumanandTrevorDarrellComputerScienceandArti?cialIntelligenceLaboratoryMassachusettsInstituteofTechnologyCambridge,MA,02139Abstractpolynomialcomplexityforeachdatabasememberagainstthequeryshap
2、e.Hierarchicalsearchmethods,pruning,orWeightedgraphmatchingisagoodwaytoalignapairofthetriangleinequalitymaybeemployed,yetquerytimesshapesrepresentedbyasetofdescriptivelocalfeatures;arestilllinearinthesizeofthedatabaseintheworstcase,thesetofcorrespondence
3、sproducedbytheminimumcostandindividualcomparisonsmaintaintheirhighcomplexitymatchingbetweentwoshapesfeaturesoftenrevealshowregardless.similartheshapesare.However,duetothecomplexityofToaddressthecomputationalcomplexityofcurrentcomputingtheexactminimumcost
4、matching,previousal-correspondence-basedshapematchingalgorithms,wepro-gorithmscouldonlyrunef?cientlywhenusingalimitedposeacontourmatchingalgorithmthatincorporatesre-numberoffeaturespershape,andcouldnotscaletoper-centlydevelopedapproximationtechniquesande
5、nablesfastformretrievalsfromlargedatabases.Wepresentacon-shape-basedsimilarityretrievalfromlargedatabases.Wetourmatchingalgorithmthatquicklycomputesthemin-treatcontourmatchingasagraphmatchingproblem,andimumweightmatchingbetweensetsofdescriptivelocaluseth
6、eEarthMover’sDistance(EMD)–theminimumcostfeaturesusingarecentlyintroducedlow-distortionembed-thatisnecessarytotransformoneweightedpointsetintodingoftheEarthMoversDistance(EMD)intoanormedanother–asametricofsimilarity.Weembedtheminimumspace.Givenanovelembe
7、ddedcontour,thenearestneigh-weightmatchingofcontourfeaturesintoL1viatheEMDborsinadatabaseofembeddedcontoursareretrievedinembeddingof[11],andthenemployapproximatenearestsublineartimeviaapproximatenearestneighborssearchneighbor(NN)searchtoretrievetheshapes
8、thataremostwithLocality-SensitiveHashing(LSH).Wedemonstrateoursimilartoanovelquery.Theembeddingstepalonere-shapematchingmethodonadatabaseof136,500imagesofducesthecomplexityofcomputingalow-costcorrespon-human?gures.Ourmetho
