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基于ORB特征的图像检索技术研究与应用的中期报告.docx

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基于ORB特征的图像检索技术研究与应用的中期报告 Abstract Inrecentyears,thedevelopmentofcomputervisiontechnologyhasmadegreatachievements,andimageretrievaltechnologybasedonfeaturematchinghasbecomeanimportantbranchofcomputervision.ORBfeatureisanexcellentmethodforimagefeatureextractionandmatchingduetoitshighspeedandstrongrobustness.Inthisreport,wesummarizetheresearchstatusofORBfeature-basedimageretrievaltechnology,analyzetheadvantagesanddisadvantagesofORB,andproposesomemethodstoimprovetheaccuracyofimageretrievalbasedonORBfeatures. Introduction Imageretrievaltechnologyismainlyusedtostore,search,andretrieveimagesbasedontheirvisualcontent,whichisaveryimportanttechnologyinthefieldofcomputervision.Feature-basedimageretrievaltechnologyisacommonmethodforimageretrieval,whichisbasedontheextraction,matching,andindexingofimagefeatures.ORB(OrientedFASTandRotatedBRIEF)featureisapopularmethodforimagefeatureextraction,whichcanprovideaveryrobustandefficientfeatureextractionandmatchingstrategy. ResearchStatusofORBFeature-BasedImageRetrievalTechnology ORBfeatureisausefulmethodforimagefeatureextraction,whichcombinesFAST(FeaturesfromAcceleratedSegmentTest)andBRIEF(BinaryRobustIndependentElementaryFeatures)methods.Byusingthesemethods,ORBfeaturecanprovideafasterandmorerobustfeatureextractionandmatchingprocessthantraditionalfeatureextractionandmatchingmethods. SeveralresearchstudieshavebeenconductedonORBfeature-basedimageretrievaltechnology.Forexample,Y.Songetal.proposedanimageretrievalmethodbasedonORBfeaturesandBoW(BagofWords)model[1].Thismethodcanachieveahighretrievalaccuracy,andhasagoodperformanceinlarge-scaleimageretrieval.L.Altafetal.proposedamultimediaretrievalsystembasedonORBfeaturesandCNN(ConvolutionalNeuralNetwork)features[2].Thissystemcanachieveahighaccuracyinimageandvideoretrieval,andhasagoodperformanceinmultimediaretrieval. AdvantagesandDisadvantagesofORBFeature-BasedImageRetrievalTechnology ORBfeaturehasseveraladvantagesanddisadvantagesinimageretrievaltechnology.TheadvantagesofORBfeatureareasfollows: 1