图象检索中若干距离度量算法研究的中期报告-第一文库 | 海量文档资源下载与分享平台

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图象检索中若干距离度量算法研究的中期报告Abstract:Thisreportfocusesonthestudyofdistancemetricalgorithmsinimageretrieval,includingEuclideandistance,Manhattandistance,Cosinesimilarity,andMahalanobisdistance.Acomparisonofthesealgorithmsbasedonperformance,robustness,andefficiencyisprovided.Inaddition,futuredevelopmentdirectionsandchallengesofdistancemetricalgorithmsinimageretrievalarediscussed.Introduction:Withtheincreasingamountofdigitalimagesavailable,imageretrievaltechniqueshavebecomeindispensabletoolsformanagingandorganizingtheimages.Distancemetricalgorithmsareessentialcomponentsofimageretrievalsystems,whichmeasurethesimilaritybetweenimagesbasedontheirfeatures.Theefficiencyandrobustnessofdistancemetricalgorithmsdirectlyaffecttheperformanceofimageretrievalsystems.Inthisreport,weinvestigateseveralwidelyuseddistancemetricalgorithmsinimageretrievalandcomparetheirperformancesbasedondifferentcriteria.DistanceMetricAlgorithms:Euclideandistanceisoneofthemostcommondistancemetricsusedinimageretrieval.Itmeasuresthestraight-linedistancebetweentwovectorsinthefeaturespace.Euclideandistanceissensitivetosmallchangesinthefeaturevaluesandcanbecomputationallyexpensiveforhigh-dimensionalfeaturespaces.Manhattandistance,alsoknownastheL1norm,measuresthedistancebysummingtheabsolutedifferencesbetweenelementsinthetwovectors.UnlikeEuclideandistance,Manhattandistanceislesssensitivetooutliersinhigh-dimensionalfeaturespacesandismorecomputationallyefficient.Cosinesimilaritymeasuresthesimilaritybetweentwovectorsbytakingthecosineoftheanglebetweenthem.Cosinesimilarityisparticularlysuitableforhigh-dimensionalfeaturespacesandisrelativelyinsensitivetochangesinthelengthofthevectors.Mahalanobisdistancetakesintoaccountthecovariancebetweenthefeaturedimensionsandmeasuresthedistancebetweentwovectorsinatransformedspace.Mahalanobisdistancecanhandlecorrelatedoruncorrelatedfeaturedimensionsandiseffectiveinremovingredundantinformation.Comparison:Wecomparethesefourdistancemetricalgorithmsbasedonthreeperformancecriteria:accu