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3DModelRetrievalMethodBasedonAffinityPropagationClustering

(题目中实词首字母大写,四号粗体)

LinLin*,XiaolongXie,andFangyuChen

(名前姓后,两个名字之间用连字符连接,小四号斜体)

(SchoolofMechatronicsEngineering,HarbinInstituteofTechnology,Harbin150001,China)

(单位采用小单位前,大单位后,若有多个单位需要编号,作者姓名右上角加上相应的编号,六号正体)

Abstract:Inordertoimprovetheaccuracyandefficiencyof3Dmodelretrieval,themethodbasedonaffinitypropagationclusteringalgorithmisproposed.Firstly,projectionray-basedmethodisproposedtoimprovethefeatureextractionefficiencyof3Dmodels.Basedontherelationshipbetweenmodelanditsprojection,theintersectionin3Dspaceistransformedintointersectionin2Dspace,whichreducesthenumberofintersectionandimprovestheefficiencyoftheextractionalgorithm.Infeatureextraction,multi-layerspheresmethodisanalyzed.Thetwo-layerspheresmethodmakesthefeaturevectormoreaccurateandimprovesretrievalprecision.Secondly,Semi-supervisedAffinityPropagation(S-AP)clusteringisutilizedbecauseitcanbeappliedtodifferentclusterstructures.TheS-APalgorithmisadoptedtofindthecentermodelsandthenthecentermodelcollectionisbuilt.Duringretrievalprocess,thecollectionisutilizedtoclassifythequerymodelintocorrespondingmodelbaseandthenthemostsimilarmodelisretrievedinthemodelbase.Finally,75samplemodelsfromPrincetonlibraryareselectedtodotheexperimentandthen36modelsareusedforretrievaltest.Theresultsvalidatethattheproposedmethodoutperformstheoriginalmethodandtheretrievalprecisionandrecallratiosareimprovedeffectively.

(摘要四要素:目的,过程和方法,结果,结论,小五号)

Keywords:featureextraction,projectray-basedmethod,affinitypropagationclustering,3Dmodelretrieval

(关键词3～8个,小五号)

CLCNumber:TP391.7(中图分类号必须有,小五号)

Introduction(一级标题从引言部分开始编号,以下以此类推,四号粗体)

WiththedevelopmentandwideapplicationofCAD/CAMtechnology,thenumberof3Dmodelsgrowsgreatly.Howtoretrievethedesired3Dmodelfromthemassmodelbaseefficientlyandtousethemforre-designbeesanurgentdemand.Whendesigning3Dmodelforanewproduct,thedesignersoftenneedtoretrievesimilarmodelsandrevisethemandthiswillimprovethedesignefficiency.Ifthenumberof3Dmodelissmall,itiseasytofindthesuitable3Dmodel,butifthenumberof3Dmodelsislarge,itisdifficulttofindthedesiredmodelonlybythedesigner'smemoryinashorttime.Sotheputerisrequiredtospeedupthedesignprocess.Inaddition,inotherareaswhichneedtoprocessalargenumberof3Dgeometryinformation,3Dretrievaltechnologyalsohasthewideapplication.

Featureextractionof3Dmodelisthemostimportantpartofretrievaltechnology.Featureextractionisextractingthecharacteristicdescriptorsfromthe3Dmodelandformingafeaturevector,whichcanbeutilizedtodistinguish3Dmodels.Thesimilaritybetweentwomodelscanbecalculatedbasedonthefeaturevectors,andthenthemostsimilar3Dmodelisretrievedfromthemodelbases.Thealgorithmsof3Dmodelfeatureextractioncanbedividedintothefollowingcategories[1-3]:thealgorithmsbasedonthegeometricinformation,thealgorithmsbasedonthesummomentofthespatialandfrequencydomainandthealgorithmsbasedontopologicalrelationships.Theray-basedmethod[4]belongingtothegeometryinformationmethodhasbeenwidelyusedandmanyfeatureextractionalgorithmsarederivedfromit.However,duetothelowefficiencyofthealgorithmanditsapplicationlimitationsonthesomeissues,itshouldbeimprovedinpractice.Inordertoimprovetheefficiencyofray-basedmethod,theprojectionray-basedmethodisproposedinthispaper,whichreducestheintersectioncalculationoftriangularfacetsandrays.

Theprocessof3Dmodelretrievaliscalculatingandparingthesimilaritybetweenthefeaturevectorsof3Dmodels.Supposingthereareafeaturevectorspaceandtwofeaturevectorsand,thesimilaritycanbecalculatedbyusingthefollowingmethods:

1)statisticaldistance:

(1)

Minkowski-formdistance():

(2)

If,theabsolutedistanceis:

(3)

If,theEuclideandistanceis:

(4)

Accordingtotheaboveexpressions,thelarge-scalemodelbaseandhighdimensionoffeaturevectorwillleadtoahighputationalplexityof3Dretrieval.Inordertolimittheretrievalscopeandimprovetheefficiency,theclusteralgorithmisappliedinthepapertofindtherepresentativemodelsfromthemodelbaseandtheyareusedtoclassifythequerymodelintothecorrectcluster.Thentheretrievalisexecutedonlyincluster,soitcanimprovetheefficiency.

K-meansclustering[5-6]isthemostwidelyappliedmethod,anditcandealwithlarge-scaledatawithfastiterationspeed.ButK-meansalgorithmissensitivetoinitialclustercentersandeasytofallintothelocalminimum.Thereforeitisrequiredtorunmanytimeswiththedifferentinitializationtofindthebestclusteringresults.However,thisstrategyiseffectiveonlywithasmallnumberofclustersandthebetterinitialization.

Supportvectormachinetechnology[7](SVM)haswideapplicationinthefieldofdataclassificationanditoverestheproblemsofhighdimensionandlocalminimum.However,SVMisasupervisedlearningalgorithmandalarge-scalequadraticprogramming.Astoamulti-classificationpr

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