《计算机专业英语》chapter11 Content-based Image RetrievalCBIR

Chapter11

Content-basedImageRetrieval（CBIR)ThinkAbout…Whatiscontent-basedimageretrieval?Whatdoestheterm“content”mean?Whatarelowlevelimageretrieval,regionbasedimageretrieval,andsemanticimageretrievalrespectively?Fromthehistoricoverview,howhasCBIRevolved?Whatdoesitmeanbymultimediainformationretrieval?WhatResearchAreasAreInvolvedIn?

Computervision,patternrecognition,imageprocessing,datamining,machinelearning,human-computerinteraction,artificialintelligenceApplication:digitalmuseum/libraries,safetyofsociety,image/videocopydetection,GIS,medicine,education,entertainment,WWW,tonamejustafewDigitalImageRetrievalSearchfordigitalimagesinlargedatabasesFirstgeneration:laborious,subjectiveMetadata(captionsorkeywords)→ImageSecondgeneration(content-based):objectiveImagecontents→ImageCurrentway(semantic):subjective+objectiveImagecontents+semanticfeature→ImageOurway:Imagecontents+semanticfeature+keywords/captions→ImageSemanticgapWhatIsContent-basedImageRetrieval“Content-based”meansthatthesearchwillanalyzetheactualcontentsoftheimage.Theterm“content”inthiscontextmightrefercolors,shapes,textures,oranyotherinformationthatcanbederivedfromtheimageitselfLowLevelImageRetrievalColorExaminingimagesbasedonthecolorstheycontainisoneofthemostwidelyusedtechniquesbecauseitdoesnotdependonimagesizeororientation.Colorsearcheswillusuallyinvolvecomparingcolorhistograms,thoughthisisnottheonlytechniqueinpracticeColor

SpaceRGBLightness(亮度，即明暗）Hue（色调，即光的颜色）Saturation（饱和度，即颜色的深浅）Chrominance

(色度）LowLevelImageRetrievalShapeShapedoesnotrefertotheshapeofanimagebuttotheshapeofaparticularregionthatisbeingsoughtout.Shapeswilloftenbedeterminedfirstapplyingsegmentationoredgedetectiontoanimage.Insomecasesaccurateshapedetectionwillrequirehumaninterventionbecausemethodslikesegmentationareverydifficulttocompletelyautomate.EdgeDetectionLowLevelImageRetrievalTextureTexturemeasureslookforvisualpatternsinimagesandhowtheyarespatiallydefined.Texturesarerepresentedbytexelswhicharethenplacedintoanumberofsets,dependingonhowmanytexturesaredetectedintheimage.Thesesetsnotonlydefinethetexture,butalsowhereintheimagethetextureislocatedTextureTextureCoarseness(粗糙度）Contrast

(对比度）Directionality(方向度）Linearity(线性度）Regularity(规整度）Roughness(粗略度）RegionBasedImageRetrievalSemanticImageRetrievalHumanjudgementofimagesimilarityissubjective.ThereforelatestresearchfocusonderivingsemanticfeaturesusingmachinelearningtechniquestonarrowdownthesemanticgapSemanticModelLowlevelfeatures(mixtureofcolor,shape,texture,e.g.redcircle)Objects,e.g.amanSpatialrelationshipbetweentheobjects,e.g.amaninfrontofthehouseEnvironment,e.g.sandAction,e.g.runFeeling,e.g.happySemanticModelLowlevelfeatures:color,shape…Objects:person,ball…Spatialrelationship:person’spositions…Environment:sand,bluesky…Action:playvolleyballFeeling:relax,happy…SemanticFeatureRetrievalAutomaticimageannotationBuildasemanticspaceContent-basedImageRetrievalFromtheabovehistoricoverview,itcanbeseenhowCBIRhasevolvedfromlowlevelimageretrievaltoregionbasedimageretrievalandtosemanticimageretrievalDatabasesStructureofCBIRDigitalimagesFeatureextractionUserQueryinterfaceSearchengineImagedatabaseFeaturedatabaseKnowledgedatabaseImageRetrievalMethodsQuerybyexternalpictorialexampleBrandsearch,fingermarksearchQuerybyinternalpictorialexampleQuerybysketchKeywords/captionsCombinationoftheabovemethodsEvaluation

dbcaPrecisionRecallPVREffectivenessEfficiencyFlexibilityAUserInterfaceofImageRetrieval

FutureD