摘要
Thecombinationofvisualandtextualinformationinimageretrievalremarkablyalleviatesthesemanticgapoftraditionalimageretrievalmethods,andthusithasattractedmuchattentionrecently.Imageretrievalbasedonsuchacombinationisusuallycalledthecontent-and-textbasedimageretrieval(CTBIR).Nevertheless,existingstudiesinCTBIRmainlymakeeffortsonimprovingtheretrievalquality.Tothebestofourknowledge,littleattentionhasbeenfocusedonhowtoenhancetheretrievalefficiency.Nowadays,imagedataiswidespreadandexpandingrapidlyinourdailylife.Obviously,itisimportantandinterestingtoinvestigatetheretrievalefficiency.Tothisend,thispaperpresentsanefficientimageretrievalmethodnamedCATIRI(content-and-textbasedimageretrievalusingindexing).CATIRIfollowsathree-phasesolutionframeworkthatdevelopsanewindexingstructurecalledMHIM-tree.TheMHIM-treeseamlesslyintegratesseveralelementsincludingManhattanHashing,Invertedindex,andM-tree.TouseourMHIM-treewiselyinthequery,wepresentasetofimportantmetricsandrevealtheirinherentproperties.Basedonthem,wedevelopatop-kqueryalgorithmforCTBIR.ExperimentalresultsbasedonbenchmarkimagedatasetsdemonstratethatCATIRIoutperformsthecompetitorsbyanorderofmagnitude.
出版日期
2019年02月12日（中国期刊网平台首次上网日期，不代表论文的发表时间）
