基于多特征信息融合的图像检索技术研究精选

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1、华侨大学硕士学位论文基于多特征信息融合的图像检索技术研究姓名：叶宇光申请学位级别：硕士专业：计算机应用技术导教师：陈锻生20060915ABSTRACTContentBasedImageRetrieval(CBIR)hasbeenaveryactiveresearchareasince1990's.Inthecontentbasedimageretrieval，itisretrievedbysinglevisualcharacteratthebeginningoftheresearch，suchas:color，textureandfigurecharacters.C

2、olorandtexturecharacteraretwoveryimportantandwidelyusedimagecharactersinthecontentbasedimageretrieval.Sofirstlythesetwofeaturesarerespectivelydiscussedinthispaper.Andatthesametimeinvariabletheoryhasachievedsuccessfulapplicationinthecontentbasedimagedataretrievalinrecentyears，soinvariant

3、characterofimageretrievalisalsodiscussedinthispaper.First，twocolorspaces(RGBandHSV)，threecolorhsitogramsmethods(traditionhsitogram，accumulationhsitogram，localaccumulationhsitogram)，andcolormatchingalgorithmarediscussedinthispaper.Thentexturefeatureisanalyzedanddiscussed，andgaborwavelett

4、ransformationisusedtopickuptexturefeature.Afterwards，thispaperbrieflydescribesrelatedcontentofinvarianttheory，andchoosestheinvariantdescriptorofcombiningwithDFTandlogpolartransformationtobeimagecharacterdescriptor，anddoestherelevantimageretrievalexperiment.Relevancefeedbacktechniquehasb

5、eenanimportantapproachinimageretrievaldiscussedinthispaper.Meanwhilethispaperquotesanovelimageretrievalmethodofsupportvectormachinebasedrelevancefeedback,andtheproposedapproachpartiallysolvesthesampleinsufficiencyproblemthatexistsinSVMbasedrelevancefeedbackalgorithm,andshowtheapproachha

6、sgoodperformanceforretrieval.Theimageretrievalfusingwithmulti-characterisaprogressdirection.Howeverhowtoefficientlyfuseandaffirmtheweightvalueamongdifferentcharactersinthetraditionalrelevancefeedbackisabigproblem.Thispaper,withtheideaofinformationfusion,proposesatwo-layerSVMclassifiermo

7、deltoimprovetheprecisionoftheclassificationandusesSVM-basedInformationFusionMachinetoefficientlysolvetheproblemoffusingmulti-characterintheimageretrieval.Itradicallysolvesthebigproblemofdistillingandfusingmulti-character.Manyexperimentsprovethatthethreeimagecharacters,thatis,in