LectureNotesinComputerScience9516
CommencedPublicationin1973
FoundingandFormerSeriesEditors: GerhardGoos,JurisHartmanis,andJanvanLeeuwen
EditorialBoard
DavidHutchison
LancasterUniversity,Lancaster,UK
TakeoKanade
CarnegieMellonUniversity,Pittsburgh,PA,USA
JosefKittler UniversityofSurrey,Guildford,UK
JonM.Kleinberg
CornellUniversity,Ithaca,NY,USA
FriedemannMattern
ETHZurich,Zürich,Switzerland
JohnC.Mitchell
StanfordUniversity,Stanford,CA,USA
MoniNaor
WeizmannInstituteofScience,Rehovot,Israel
C.PanduRangan
IndianInstituteofTechnology,Madras,India
BernhardSteffen TUDortmundUniversity,Dortmund,Germany
DemetriTerzopoulos UniversityofCalifornia,LosAngeles,CA,USA
DougTygar UniversityofCalifornia,Berkeley,CA,USA
GerhardWeikum
MaxPlanckInstituteforInformatics,Saarbrücken,Germany
Moreinformationaboutthisseriesathttp://www.springer.com/series/7409
QiTian • NicuSebe
Guo-JunQi • BenoitHuet
RichangHong • XueliangLiu(Eds.)
MultiMediaModeling
22ndInternationalConference,MMM2016
Miami,FL,USA,January4–6,2016
Proceedings,PartI
Editors
QiTian
UniversityofTexasatSanAntonio SanAntonio,TX USA
NicuSebe
DepartmentofInformationEngineering UniversityofTrento Povo,Trento Italy
Guo-JunQi
EECS UniversityofCentralFlorida Orlando,FL USA
BenoitHuet EURECOM
Sophia-Antipolis
France
RichangHong
HefeiUniversityofTechnology
Hefei,Anhui
China
XueliangLiu
SchoolofComputingandInformation
HefeiUniversityofTechnology
Hefei,Anhui
China
ISSN0302-9743ISSN1611-3349(electronic)
LectureNotesinComputerScience
ISBN978-3-319-27670-0ISBN978-3-319-27671-7(eBook) DOI10.1007/978-3-319-27671-7
LibraryofCongressControlNumber:2015957238
LNCSSublibrary:SL3 – InformationSystemsandApplications,incl.Internet/Web,andHCI
© SpringerInternationalPublishingSwitzerland2016
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Preface
The22ndInternationalConferenceonMultimediaModeling(MMM2016)washeldin Miami,USA,January4–6,2016,andwashostedbytheUniversityofCentralFlorida atOrlando,USA.MMMisaleadinginternationalconferenceforresearchersand industrypractitionerstosharetheirnewideas,originalresearchresults,andpractical developmentexperiencesfromallmultimedia-relatedareas.UniversityofCentral FloridaisaSpace-Grantuniversityandhasmadenotedresearchcontributionstodigital media,engineering,andcomputerscience.
MMM2016featuredacomprehensiveprogramincludingthreekeynotetalks,eight oralpresentationsessions,twopostersessions,onedemosession, fivespecialsessions, andtheVideoBrowserShowdown(VBS).The168submissionsfromauthorsof20 countriesincludedalargenumberofhigh-qualitypapersinmultimediacontentanalysis,multimediasignalprocessingandcommunications,andmultimediaapplications andservices.Wethankour130TechnicalProgramCommitteememberswhospent manyhoursreviewingpapersandprovidingvaluablefeedbacktotheauthors.Fromthe totalof117submissionstothemainconferenceandbasedonatleastthreereviewsper submission,theprogramchairsdecidedtoaccept32regularpapers(27.8%)and30 posterpapers(25.6%).Intotal,38paperswerereceivedfor5specialsessions,with20 beingselected,and11submissionswerereceivedforademosession,with7being selected.Videobrowsingsystemsofnineteamswereselectedforparticipationinthe VBS.Theauthorsofacceptedpaperscomefrom17countries.Thisvolumeofthe conferenceproceedingscontainstheabstractsofthreeinvitedtalksandalltheregular, poster,specialsession,anddemopapers,aswellasspecialdemopapersoftheVBS. MMM2016includedthefollowingawards:theBestPaperAward,theBestStudent PaperAward,andthewinneroftheVBScompetition.
Thetechnicalprogramisanimportantaspectbutonlyprovidesitsfullimpactif complementedbychallengingkeynotes.Wewereextremelypleasedandgratefulto havethreeexceptionalkeynotespeakers,WenGao(ACM/IEEEFellow),ChangWen Chen(IEEEFellow),andChangshengXu(IEEEFellow),acceptourinvitationand presentinterestingideasandinsightsatMMM2016.
Weareheavilyindebtedtomanyindividualsfortheirsignificantcontributions.We thanktheMMMSteeringCommitteefortheirinvaluableinputandguidanceoncrucial decisions.Wewishtoacknowledgeandexpressourdeepestappreciationtothe organizingchairs,XueliangLiuandLumingZhang,thespecialsessionchairs,WenHuangChen,HaojieLiandRongrongJi,thepanelchair,Tat-SengChua,thedemo chairs,CathalGurrinandBjörn ÞórJónsson,theVBSchairs,KlausSchöffmannand WernerBailer,thepublicitychairs,Yu-GangJiang,ShuichengYan,HengtaoShen, ZhengjunZha,andShengWu,thepublicationchairs,NaZhaoandZechaoLi,andlast butnotleasttheWebmaster,JunHe.Withouttheireffortsandenthusiasm,MMM2016 wouldnothavebecomeareality.Moreover,wewanttothankoursponsorthe
UniversityofCentralFlorida.Finally,wewishtothankallcommitteemembers, reviewers,sessionchairs,studentvolunteers,andsupporters.Theircontributionsare muchappreciated.
January2016Guo-JunQi
BenoitHuet
RichangHong
NicuSebe
QiTian
Organization
MMM2016wasorganizedbytheUniversityofCentralFlorida,USA.
MMM2016SteeringCommittee
PhoebeChenLaTrobeUniversity,Australia Tat-SengChuaNationalUniversityofSingapore
ShiqiangYangTsinghuaUniversity,China
KiyoharuAizawaUniversityofTokyo,Japan
NoelE.O’ConnorDublinCityUniversity,Ireland
CessG.M.SnoekUniversityofAmsterdam,TheNetherlands
MengWangHefeiUniversityofTechnology,China R.ManmathaUniversityofMassachusetts,USA CathalGurrinDublinCityUniversity,Ireland KlausSchoeffmannKlagenfurtUniversity,Austria BenoitHuetEurecom,France
MMM2016OrganizingCommittee
GeneralCo-chairs
QiTianUniversityofTexasatSanAntonio,USA NicuSebeUniversityofTrento,Italy
ProgramCo-chairs
GuojunQiUniversityofCentralFlorida,USA BenoitHuetEurecom,France
RichangHongHefeiUniversityofTechnology,China
OrganizingCo-chairs
XueliangLiuHefeiUniversityofTechnology,China Luming,ZhangNationalUniversityofSingapore,Singapore
SpecialSessionCo-chairs
Wen-HuangChengAcademiaSinica,Taiwan
HaojieLiDalianUniversityofTechnology,China RongrongJiXiamenUniversity,China
DemoSessionCo-chairs
CathalGurrinDublinCityUniversity,Ireland
Björn ÞórJónssonReykjavíkUniversity,Iceland
PublicationCo-chairs
NaZhaoNationalUniversityofSingapore,Singapore ZechaoLiNanjingUniversityofScienceandTechnology,China
PublicityCo-chairs
Yu-GangJiangFudanUniversity,China ShuichengYanNationalUniversityofSingapore,Singapore HengtaoShenUniversityofQueensland,Australia ZhengjunZhaChineseAcademyofSciences,China ShengWuGoogle,USA
PanelChair
Tat-SengChuaNationalUniversityofSingapore,Singapore
VideoSearchShowcaseCo-chairs
WernerBailerJoanneumResearch,Graz,Austria KlausSchoeffmannKlagenfurtUniversity,Austria
WebMaster
JunHeHefeiUniversityofTechnology,China
TechnicalProgramCommittee
SelimBalcisoySabanciUniversity,Turkey YingboLiEcoleNormaleSuperieure,France LifengSunTsinghuaUniversity,China CathalGurrinDublinCityUniversity,Ireland HaojieLiDalianUniversityofTechnology,China RainerLienhartUniversityofAugsburg,Germany RossanaDamianoUniversityofTurin,Italy Zheng-JunZhaInstituteofIntelligentMachines,CAS,China VincentCharvillatUniversityofToulouse,France LiqiangNieNationalUniversityofSingapore,Singapore WolfgangHurstUtrechtUniversity,TheNetherlands Wei-GuangTengNationalChengKungUniversity,Taiwan BoYanFudanUniversity,China
WernerBailerJoanneumResearch,Austria Mei-LingShyuUniversityofMiami,USA LuizFernandoGomes
Soares CatholicUniversityofRiodeJaneiro,Brazil
JoemonJoseUniversityofGlasgow,UK MyleneFariasUniversityofBrasilia,Brazil WolfgangHuerstUtrechtUniversity,TheNetherlands
XuranZhaoZhejiangGongshangUniversity,China NaokoNittaOsakaUniversity,Japan JunYuHangdianUniversity,China
GeorgThallingerJoanneumResearch,Austria Yu-GangJiangFudanUniversity,China MarkusKoskelaUniversityofHelsinki,Finland JingdongWangMicrosoftResearchAsia,China ZiyuGuanWestNorthUniversity,China WilliamGroskyUniversityofMichigan,USA GeorgesQuenotLIG/IMAG,France
Duy-DinhLeNationalInstituteofInformatics,Japan HenningMullerTheUniversityofAppliedSciencesandArts ofWesternSwitzerland Wen-HsiangTsaiNationalChiaoTungUniversity,Taiwan AnantBaijalSamsung,Korea
KuiyuanYangMicrosoftResearchAsia,China ShengTangChineseAcademyofSciences,China ShinIchiSatohNationalInstituteofInformatics,Japan MarcelWorringUniversityofAmsterdam,TheNetherlands AjayDivakaranSarnoffCorporation,USA PengCuiTsinghuaUniversity,China HanwangZhangNationalUniversityofSingapore,Singapore JitaoSangChineseAcademyofSciences,China RichangHongHefeiUniversityofTechnology,China HaraldKoschUniversityofPassau,Germany ShikuiWeiBeijingJiaotongUniversity,China BoLiuUniversityofRutgers,USA WolfgangEffelsbergUniversityofMannheim,Germany NoelE.O.ConnorDublinCityUniversity,Ireland LuFangUniversityofScienceandTechnology,China XiaoWuWestSouthJiaotongUniversity,China XinmeiTianUniversityofScienceandTechnology,China XueliangLiuHefeiUniversityofTechnology,China RuiMinCognitec,Germany
JiroKattoWasedaUniversity,Japan JianChengChineseAcademyofSciences,China VincentOriaNewJerseyInstituteofTechnology,USA DaliborMitrovicViennaUniversityofTechnology,Austria MilanBjelicaUniversityofBelgrade,Serbia AndreasHenrichUniversityofBamberg,Germany ShijieHaoHefeiUniversityofTechnology,China PhivosMylonasNationalTechnicalUniversityofAthens,Greece FengWangEastChinaNormalUniversity,China AllanHanburyViennaUniversityofTechnology,Austria JinqiaoWangChineseAcademyofSciences,China TianzhuZhangChineseAcademyofSciences,China
YifanZhangChineseAcademyofSciences,China Wei-TaChuNationalChungChengUniversity,Taiwan WesleyDeNeveJoanneumResearch,Austria JeanMartinetUniversityofLille,France
OgnjenArandjelovicTrinityCollegeCambridge,UK
KeijiYanaiUniversityofElectro-Communications,Japan
RongrongJiXiamenUniversity,China
JinhuiTangNanjingUniversityofScienceandTechnology,China MaiaZaharievaViennaUniversityofTechnology,Austria ChaZhangMicrosoftResearch,USA
ShiyuChangUniversityofIllinoisUrbana-Champaign,USA
LeAnUniversityofNorthCarolinaatChapelHill,USA MohanKankanhalliNationalUniversityofSingapore,Singapore ShiaiZhuUniversityofWaterloo,USA
VasileiosMezarisCERTH/ITI,Greece
YannickPrieUniversityClaudeBernardLyon1,France MichelCrucianuCNAM,France
XiaoyiJiangUniversityofMünster,Germany
Sponsors
UniversityofCentralFlorida
Contents – PartI
RegularPapers
VideoEventDetectionUsingKernelSupportVectorMachine withIsotropicGaussianSampleUncertainty(KSVM-iGSU).............3 ChristosTzelepis,VasileiosMezaris,andIoannisPatras
VideoContentRepresentationUsingRecurringRegionsDetection........16 LukasDiemandMaiaZaharieva
GroupFeatureSelectionforAudio-BasedVideoGenreClassification......29 GerhardSageder,MaiaZaharieva,andChristianBreiteneder
ComputationalCartoonist:AComic-StyleVideoSummarization SystemforAnimeFilms.....................................42 TsukasaFukusato,TatsunoriHirai,ShunyaKawamura, andShigeoMorishima
ExploringtheLongTailofSocialMediaTags......................51 SvetlanaKordumova,JanvanGemert,andCeesG.M.Snoek
VisualAnalysesofMusicDownloadHistory:UserStudies.............63 DongLiuandJingxianZhang
PersonalizedAnnotationforMobilePhotosBasedonUser ’sSocialCircle...76 YanhuiHong,TiandiChen,KangZhang,andLifengSun
UtilizingSensor-SocialCuestoLocalizeObjects-of-Interest inOutdoorUGVs..........................................88 YingjieXia,LumingZhang,LiqiangNie,andWenjingGeng
NEWSMAN:UploadingVideosoverAdaptiveMiddleboxestoNews ServersinWeakNetworkInfrastructures..........................100 RajivRatnShah,MohamedHefeeda,RogerZimmermann, KhaledHarras,Cheng-HsinHsu,andYiYu
ComputationalFaceReader...................................114 XiangboShu,LiyanZhang,JinhuiTang,Guo-SenXie, andShuichengYan
PosedandSpontaneousExpressionRecognitionThroughRestricted BoltzmannMachine.........................................127 ChongliangWuandShangfeiWang
DFRS:ALarge-ScaleDistributedFingerprintRecognitionSystem BasedonRedis............................................138 BingLi,ZhenHuang,JinbangChen,YifanYuan,andYuxingPeng
LogoRecognitionviaImprovedTopologicalConstraint................150 PanpanTangandYuxinPeng
CompoundFigureSeparationCombiningEdgeandBandSeparator Detection................................................162 MarioTaschwerandOgeMarques
CameraNetworkBasedPersonRe-identificationbyLeveraging Spatial-TemporalConstraintandMultipleCamerasRelations............174 WenxinHuang,RuiminHu,ChaoLiang,YiYu,ZhengWang, XianZhong,andChunjieZhang
GlobalContrastBasedSalientRegionBoundarySampling forActionRecognition......................................187 ZengminXu,RuiminHu,JunChen,HuafengChen,andHongyangLi
ElasticEdgeBoxesforObjectProposalonRGB-DImages.............199 JingLiu,TongweiRen,andJiaBei
PairingContourFragmentsforObjectRecognition...................212 WeiZheng,QianZhang,ZhixuanLi,andJunjunXiong
InstanceSearchwithWeakGeometricCorrelationConsistency...........226 ZhenxingZhang,RamiAlbatal,CathalGurrin,andAlanF.Smeaton
Videopedia:LectureVideoRecommendationforEducationalBlogs UsingTopicModeling.......................................238 SubhasreeBasu,YiYu,VivekK.Singh,andRogerZimmermann
TowardsTraining-FreeRefinementforSemanticIndexingofVisual Media..................................................251 PengWang,LifengSun,ShiqangYang,andAlanF.Smeaton
DeepLearningGenericFeaturesforCross-MediaRetrieval.............264 XindiShang,HanwangZhang,andTat-SengChua
Cross-MediaRetrievalviaSemanticEntityProjection.................276 LeiHuangandYuxinPeng
VisualRe-rankingThroughGreedySelectionandRankFusion..........289 BinLin,AiWei,andXinmeiTian
No-referenceImageQualityAssessmentBasedonStructural andLuminanceInformation...................................301 QiaohongLi,WeisiLin,JingtaoXu,YumingFang, andDanielThalmann
LearningMultipleViewswithOrthogonalDenoisingAutoencoders........313 TengQiYe,TianchunWang,KevinMcGuinness,YuGuo, andCathalGurrin
FastNearestNeighborSearchintheHammingSpace.................325 ZhanshengJiang,LingxiXie,XiaotieDeng,WeiweiXu, andJingdongWang
SOMH:ASelf-OrganizingMapBasedTopologyPreservingHashing Method.................................................337 Xiao-LongLiang,Xin-ShunXu,LizhenCui,ShanqingGuo, andXiao-LinWang
DescribingImageswithOntology-AwareDictionaryLearning...........349 ChengyueZhangandYahongHan
QualityAnalysisonMobileDevicesforReal-TimeFeedback............359 StefanieWechtitsch,HannesFassold,MarcusThaler, KrzysztofKozłowski,andWernerBailer
InteractiveSearchinVideo:NavigationWithFlick Gesturesvs.Seeker-Bars.....................................370 KlausSchoeffmann,MarcoA.Hudelist,BonifazKaufmann, andKevinChromik
Second-LayerNavigationinMobileHypervideoforMedicalTraining......382 BrittaMeixnerandMatthiasGold
PosterPapers
ReverseTestingImageSetModelBasedMulti-viewHumanAction Recognition..............................................397 Z.Gao,Y.Zhang,H.Zhang,G.P.Xu,andY.B.Xue
FaceImageSuper-ResolutionThroughImprovedNeighborEmbedding.....409 KebinHuang,RuiminHu,JunjunJiang,ZhenHan,andFengWang
AdaptiveMultichannelReductionUsingConvexPolyhedralLoudspeaker Array...................................................421 LingkunZhang,RuiminHu,DengshiLi,XiaochenWang, andWeipingTu
DominantSetBasedDataClusteringandImageSegmentation...........432 JianHou,ChunshiSha,HongxiaCui,andLeiChi
AnR-CNNBasedMethodtoLocalizeSpeechBalloonsinComics........444 YongtaoWang,XichengLiu,andZhiTang
FacialAgeEstimationwithImagesintheWild.....................454
MingZou,JianweiNiu,JinpengChen,YuLiu,andXiaokeZhao
FastVisualVocabularyConstructionforImageRetrievalUsing Skewed-Splitk-dTrees......................................466
IliasGialampoukidis,StefanosVrochidis,andIoannisKompatsiaris
OGB:ADistinctiveandEfficientFeatureforMobileAugmented Reality..................................................478
XinYang,XinggangWang,andKwang-Ting(Tim)Cheng
LearningRelativeAestheticQualitywithaPairwiseApproach...........493 HaoLvandXinmeiTian
RobustCrowdSegmentationandCountinginIndoorScenes............505
RenYang,HuazhongXu,andJinqiaoWang
RobustSketch-BasedImageRetrievalbySaliencyDetection............515 XiaoZhangandXuejinChen
ImageClassificationUsingSpatialDifferenceDescriptorUnderSpatial PyramidMatchingFramework.................................527 YuhuiLi,JiuchengXu,YifanZhang,ChunjieZhang,HongshengYin, andHanqingLu
ExploringRelationshipBetweenFaceandTrustworthyImpressionUsing Mid-levelFacialFeatures.....................................540
YanYan,JieNie,LeiHuang,ZhenLi,QingleiCao,andZhiqiangWei
Edit-BasedFontSearch......................................550
KenIshibashiandKazunoriMiyata
PrivateVideoForegroundExtractionThroughChaoticMappingBased EncryptionintheCloud......................................562
XinJin,KuiGuo,ChenggenSong,XiaodongLi,GengZhao,JingLuo, YuzhenLi,YingyaChen,YanLiu,andHuaichaoWang
EvaluatingAccessMechanismsforMultimodalRepresentations ofLifelogs...............................................574
ZhengweiQiu,CathalGurrin,andAlanF.Smeaton
AnalysisandComparisonofInter-ChannelLevelDifferenceandInteraural LevelDifference...........................................586
TingzhaoWu,RuiminHu,LiGao,XiaochenWang,andShanfaKe
AutomaticScribbleSimulationforInteractiveImageSegmentation Evaluation...............................................596
BingjieJiang,TongweiRen,andJiaBei
Multi-modalImageRe-rankingwithAutoencodersandClickSemantics....609 ChaohuiTang,QingxinZhu,ChaoqunHong,andJunYu
Sketch-BasedImageRetrievalwithaNovelBoVWRepresentation........621 ChengJin,ChenjieLi,ZhemingWang,YuejieZhang,andTaoZhang
Symmetry-AwareHumanShapeCorrespondenceUsingSkeleton.........632 ZongyiXuandQianniZhang
XTemplate4.0:ProvidingAdaptiveLayoutsandNestedTemplates forHypermediaDocuments...................................642
GlaucoF.Amorim,JoelA.F.dosSantos, andDéboraC.Muchaluat-Saade
LevelRatioBasedInterandIntraChannelPredictionwithApplication toStereoAudioFrameLossConcealment.........................654 YuhongYang,YanyeWang,RuiminHu,HongjiangYu,LiGao, andSongWang
DepthMapCodingbyModelingtheLocalityandLocalCorrelation ofViewSynthesisDistortionin3-DVideo........................662 QiongXue,XuguangLan,andMengYang
DiscriminativeFeatureLearningwithanOptimalPatternModelforImage Classification.............................................675 LijuanLiu,YuBao,HaojieLi,XinFan,andZhongxuanLuo
SignLanguageRecognitionBasedonTrajectoryModelingwithHMMs....686 JunfuPu,WengangZhou,JihaiZhang,andHouqiangLi
MusicMixer:AutomaticDJSystemConsideringBeatandLatentTopic Similarity................................................698 TatsunoriHirai,HironoriDoi,andShigeoMorishima
AdaptiveSynopsisofNon-HumanPrimates’ SurveillanceVideoBasedon BehaviorClassification......................................710 DongqiCai,FeiSu,andZhichengZhao
APacketSchedulingMethodforMultimediaQoSProvisioning..........722 JinbangChen,ZhenHuang,MartinHeusse, andGuillaumeUrvoy-Keller
RobustObjectTrackingUsingValidFragmentsSelection..............738 JinZheng,BoLi,PengTian,andGangLuo
SpecialSessionPosterPapers
ExploringDiscriminativeViewsfor3DObjectRetrieval...............755 DongWang,BinWang,SichengZhao,HongxunYao,andHongLiu
WhatCatchesYourEyesasYouMoveAround?OntheDiscovery ofInterestingRegionsintheStreet..............................767 Heng-YuChi,Wen-HuangCheng,Chuang-WenYou, andMing-SyanChen
BagDetectionandRetrievalinStreetShots........................780 ChongCao,YuningDu,andHaizhouAi
TVCommercialDetectionUsingSuccessBasedLocallyWeightedKernel Combination..............................................793
RaghvendraKannaoandPrithwijitGuha
Frame-WiseContinuity-BasedVideoSummarizationandStretching.......806 TatsunoriHiraiandShigeoMorishima
RespirationMotionStateEstimationon4DCTRibCageImages.........818 ChaoXie,WengangZhou,WeipingDing,HouqiangLi,andWeipingLi
Location-AwareImageClassification.............................829 XinggangWang,XinYang,WenyuLiu,ChenDuan, andLonginJanLatecki
EnhancementforDust-SandStormImages.........................842 JianWang,YanweiPang,YuqingHe,andChangshuLiu
UsingInstagramPictureFeaturestoPredictUsers’ Personality...........850 BruceFerwerda,MarkusSchedl,andMarkoTkalcic
ExtractingVisualKnowledgefromtheInternet:MakingSenseofImage Data...................................................862 YazhouYao,JianZhang,Xian-ShengHua,FuminShen, andZhenminTang
OrderingofVisualDescriptorsinaClassifierCascadeTowardsImproved VideoConceptDetection.....................................874 FoteiniMarkatopoulou,VasileiosMezaris,andIoannisPatras
SpatialConstrainedFine-GrainedColorNameforPerson Re-identification...........................................886
YangYang,YuhongYang,MangYe,WenxinHuang,ZhengWang, ChaoLiang,LeiYao,andChunjieZhang
DealingwithAmbiguousQueriesinMultimodalVideoRetrieval.........898 LucaRossetto,ClaudiuTănase,andHeikoSchuldt
CollaborativeQ-LearningBasedRoutingControlinUnstructuredP2P Networks................................................910 Xiang-JunShen,QingChang,Jian-PingGou,Qi-RongMao, Zheng-JunZha,andKeLu
AuthorIndex ............................................923
Contents – PartII
SpecialSessionPosterPapers(continued)
TransferNonnegativeMatrixFactorizationforImageRepresentation.......3 TianchunWang,TengQiYe,andCathalGurrin
SentimentAnalysisonMulti-ViewSocialData......................15 TengNiu,ShiaiZhu,LeiPang,andAbdulmotalebElSaddik
SingleImageSuper-ResolutionviaConvolutionalNeuralNetwork andTotalVariationRegularization..............................28 YanyunQu,CuitingShi,JunranLiu,LiyingPeng,andXiaofengDu
AnEffectiveFaceVerificationAlgorithmtoFuseCompleteFeatures inConvolutionalNeuralNetwork...............................39 YukunMa,JiaoyuHe,LifangWu,andWeiQi
DriverFatigueDetectionSystemBasedonDSPPlatform..............47 ZiboLi,FanZhang,GuangminSun,DequnZhao,andKunZheng
Real-TimeGrayscale-ThermalTrackingviaLaplacianSparse Representation............................................54 ChenglongLi,ShiyiHu,SihanGao,andJinTang
EfficientPerceptualRegionDetectorBasedonObjectBoundary..........66 GangWang,KeGao,YongdongZhang,andJintaoLi
1DBarcodeRegionDetectionBasedontheHoughTransform andSupportVectorMachine..................................79 ZhihuiWang,AiChen,JianjunLi,YeYao,andZhongxuanLuo
SpecialSessionPapers
Client-DrivenStrategyofLarge-ScaleSceneStreaming................93 LaixiangWen,NingXie,andJinyuanJia
SELSH:AHashingSchemeforApproximateSimilaritySearch withEarlyStopCondition....................................104 JieChen,ChengkunHe,GangHu,andJieShao
LearningHoughTransformwithLatentStructuresforJointObject DetectionandPoseEstimation.................................116 HanxiLi,XumingHe,NickBarnes,andMingwenWang
ConsensusGuidedMultipleMatchRemovalforGeometryVerification inImageRetrieval..........................................130 HongWu,XingHeng,andZenglinXu
LocalityConstrainedSparseRepresentationforCatRecognition..........140 Yu-ChenChen,ShintamiC.Hidayati,Wen-HuangCheng, Min-ChunHu,andKai-LungHua
UserProfilingbyCombiningTopicModelingandPointwiseMutual Information(TM-PMI).......................................152 LifangWu,DanWang,ChengGuo,JiananZhang, andChangwenChen
ImageRetrievalUsingColor-AwareTagonProgressiveImageSearch andRecommendationSystem..................................162 Shih-YuKu,Kai-HsiangChen,Jen-WeiHuang,andYuTsao
AdvancingIterativeQuantizationHashingUsingIsotropicPrior..........174 LaiLi,GuangcanLiu,andQingshanLiu
AnImprovedRANSACImageStitchingAlgorithmBased SimilarityDegree..........................................185 YuleGe,ChunxiaoGao,andGuoDongLiu
ANovelEmotionalSaliencyMaptoModelEmotionalAttention Mechanism...............................................197 XinmiaoDing,LuluHuang,BingLi,CongyanLang,ZhenHua, andYulingWang
AutomaticEndmemberExtractionUsingPixelPurityIndex forHyperspectralImagery....................................207 QianlanZhou,JingZhang,QiTian,LiZhuo,andWenhaoGeng
AFast3DIndoor-LocalizationApproachBasedonVideoQueries........218 GuoyuLu,YanYan,AbhishekKolagunda,andChandraKambhamettu
SmartAmbientSoundAnalysisviaStructuredStatisticalModeling........231 JialieShen,LiqiangNie,andTat-SengChua
DiscriminantManifoldLearningviaSparseCodingforImageAnalysis.....244 MengPang,BinghuiWang,XinFan,andChuangLin
AVeryDeepSequencesLearningApproachforHuman ActionRecognition.........................................256 ZhihuiLinandChunYuan
AttributeDiscoveryforPersonRe-Identification.....................268 TakayukiUmeda,YongqingSun,GoIrie,KyokoSudo, andTetsuyaKinebuchi
WhataretheLimitstoTimeSeriesBasedRecognition ofSemanticConcepts?.......................................277
PengWang,LifengSun,ShiqiangYang,andAlanF.Smeaton
TenResearchQuestionsforScalableMultimediaAnalytics.............290
Björn ÞórJónsson,MarcelWorring,JanZahálka,StevanRudinac, andLaurentAmsaleg
Shaping-UpMultimediaAnalytics:NeedsandExpectationsofMedia Professionals.............................................303
GuillaumeGravier,MartinRagot,LaurentAmsaleg,RémiBois, GrégoireJadi, ÉricJamet,LauraMonceaux,andPascaleSébillot
InformedPerspectivesonHumanAnnotationUsingNeuralSignals........315 GrahamF.Healy,CathalGurrin,andAlanF.Smeaton
DemoSessionPapers
GrillCam:AReal-TimeEatingActionRecognitionSystem.............331
KoichiOkamotoandKeijiYanai
SearchinginVideoCollectionsUsingSketchesandSampleImages –TheCineastSystem.........................................336
LucaRossetto,IvanGiangreco,SilvanHeller,ClaudiuTănase, andHeikoSchuldt
LoggerMan,aComprehensiveLoggingandVisualizationTool toCaptureComputerUsage...................................342
ZaherHinbarji,RamiAlbatal,NoelO’Connor,andCathalGurrin
E 2 SGM :EventEnrichmentandSummarizationbyGraphModel..........348 XueliangLiu,FeifeiWang,BenoitHuet,andFengWang
METU-MMDS:AnIntelligentMultimediaDatabaseSystem forMultimodalContentExtractionandQuerying....................354 AdnanYazici,SaeidSattari,TurgayYilmaz,MustafaSert, MuratKoyuncu,andElvanGulen
ApplyingVisualUserInterestProfilesforRecommendation andPersonalisation.........................................361
JiangZhou,RamiAlbatal,andCathalGurrin
Cross-ModalFashionSearch..................................367
SusanaZoghbi,GeertHeyman,JuanCarlosGomez, andMarie-FrancineMoens
VideoBrowserShowdown
IMOTION – SearchingforVideoSequencesUsingMulti-Shot SketchQueries............................................377
LucaRossetto,IvanGiangreco,SilvanHeller,ClaudiuTănase, HeikoSchuldt,StéphaneDupont,OmarSeddati,MetinSezgin, OzanCanAltıok,andYusufSahillioğlu
iAutoMotion – anAutonomousContent-BasedVideoRetrievalEngine.....383
LucaRossetto,IvanGiangreco,ClaudiuTănase,HeikoSchuldt, StéphaneDupont,OmarSeddati,MetinSezgin,andYusufSahillioğlu
SelectingUserGeneratedContentforUseinMediaProductions..........388 WernerBailer,WolfgangWeiss,andStefanieWechtitsch
VERGE:AMultimodalInteractiveSearchEngineforVideoBrowsing andRetrieval.............................................394
AnastasiaMoumtzidou,TheodorosMironidis,EvlampiosApostolidis, FoteiniMarkatopoulou,AnastasiaIoannidou,IliasGialampoukidis, KonstantinosAvgerinakis,StefanosVrochidis,VasileiosMezaris, IoannisKompatsiaris,andIoannisPatras
CollaborativeVideoSearchCombiningVideoRetrievalwithHuman-Based VisualInspection..........................................400
MarcoA.Hudelist,ClaudiuCobârzan,ChristianBeecks, RobvandeWerken,SabrinaKletz,WolfgangHürst, andKlausSchoeffmann
Multi-sketchSemanticVideoBrowser............................406
DavidKuboň,AdamBlažek,JakubLokoč,andTomáš Skopal
FacetedNavigationforBrowsingLargeVideoCollection..............412 ZhenxingZhang,WeiLi,CathalGurrin,andAlanF.Smeaton
NavigatingaGraphofScenesforExploringLargeVideoCollections......418 KaiUweBarthel,NicoHezel,andRadekMackowiak
MentalVisualBrowsing.....................................424
JunHe,XindiShang,HanwangZhang,andTat-SengChua
AuthorIndex ............................................429
RegularPapers
ChristosTzelepis1,2(B) ,VasileiosMezaris1 ,andIoannisPatras2
1 InformationTechnologiesInstitute(ITI),CERTH,57001Thermi,Greece {tzelepis,bmezaris}@iti.gr
2 QueenMaryUniversityofLondon,MileEndCampus,LondonE14NS,UK i.patras@qmul.ac.uk
Abstract. Inthispaper,weproposeanalgorithmthatlearnsfrom uncertaindataandexploitsrelatedvideosfortheproblemofeventdetection;relatedvideosarethosethatarecloselyassociated,thoughnotfully depictingtheeventofinterest.Inparticular,twoextensionsofthelinear SVMwithGaussianSampleUncertaintyarepresented,which(a)leadto non-lineardecisionboundariesand(b)incorporaterelatedclasssamples intheoptimizationproblem.Theresultinglearningmethodsareespeciallyusefulinproblemswhereonlyalimitednumberofpositiveand relatedtrainingobservationsareprovided,e.g.,forthe10Exsubtask ofTRECVIDMED,whereonlytenpositiveandfiverelatedsamples areprovidedforthetrainingofacomplexeventdetector.Experimental resultsontheTRECVIDMED2014datasetverifytheeffectivenessof theproposedmethods.
Keywords: Videoeventdetection · Veryfewpositivesamples · Related samples · Learningwithuncertainty · Kernelmethods · Relevancedegree SVMs
1Introduction
High-levelvideoeventdetectionisconcernedwithdeterminingwhetheracertainvideodepictsagiveneventornot.Typically,ahigh-level(orcomplex) eventisdefinedasaninteractionamonghumans,orbetweenhumansandphysicalobjects[16].Sometypicalexamplesofcomplexeventsarethoseprovided intheMultimediaEventDetection(MED)taskoftheTRECVIDbenchmarkingactivity[22].Forinstance,indicativecomplexeventsdefinedinMED2014 include“Attemptingabiketrick”,“Cleaninganappliance”,or“Beekeeping”, tonameafew.
Therearenumerouschallengesassociatedwithbuildingeffectivevideoevent detectors.Oneofthemisthatoftenthereisonlyalimitednumberofpositive videoexamplesavailablefortraining.Anotherchallengeisthatvideorepresentationtechniquesusuallyintroduceuncertaintyintheinputthatisfedto c SpringerInternationalPublishingSwitzerland2016 Q.Tianetal.(Eds.):MMM2016,PartI,LNCS9516,pp.3–15,2016. DOI:10.1007/978-3-319-27671-7 1
4C.Tzelepisetal.
theclassifiers,andthisalsoneedstobetakenintoconsiderationduringclassifiertraining.Inthisworkwedealwiththeproblemoflearningvideoevent detectorswhenalimitednumberofpositiveandrelated(i.e.,videosthatare closelyrelatedwiththeevent,butdonotmeettheexactrequirementsforbeing apositiveeventinstance[22])eventvideosareprovided.Forthis,weexploit theuncertaintyofthetrainingvideosbyextendingthelinearSupportVector MachinewithGaussianSampleUncertainty(LSVM-GSU),presentedin[27], inordertoarriveatnon-lineardecisionfunctions.Specifically,weextendthis versionofLSVM-GSUthatassumesisotropicuncertainty(hereafterdenoted LSVM-iGSU)intoanewkernel-basedalgorithm,whichwecallKSVM-iGSU. WealsofurtherextendKSVM-iGSU,drawinginspirationfromtheRelevance DegreekernelSVM(RD-KSVM)proposedin[28],suchthatrelatedsamples canbeeffectivelyexploitedaspositiveornegativeexampleswithautomatic weighting.WerefertothisalgorithmasRD-KSVM-iGSU.Weshowthatthe RD-KSVM-iGSUalgorithmresultsinmoreaccurateeventdetectorsthanthe stateofthearttechniquesusedinrelatedworks,suchasthestandardkernel SVMandRD-KSVM.
Thepaperisorganizedasfollows.InSect. 2 wereviewrelatedwork.In Sect. 3 thetwoproposedSVMextensionsarepresented.Videoeventdetection results,byapplicationoftheproposedKSVM-iGSUandRD-KSVM-iGSUto theTRECVIDMED2014dataset,areprovidedinSect. 4,whileconclusionsare drawnandfutureworkisdiscussedinSect. 5.
2RelatedWork
Therearemanyworksdealingwitheventdetectioninvideo(e.g.[2, 5, 7, 9, 11–15, 19, 21]),severalofthembeinginthecontextoftheTRECVIDMEDtask. Despitetheattentionthatvideoeventdetectionhasreceived,though,there isonlyalimitednumberofstudiesthathaveexplicitlyexaminedtheproblemoflearningeventdetectorsfromveryfew(e.g.10)positivetrainingexamples[13, 28],anddevelopedmethodsforaddressingthisexactproblem.In[13], forinstance,theauthorspresentVideoStory,avideorepresentationschemefor learningeventdetectorsfromafewtrainingexamplesbyexploitingfreelyavailableWebvideostogetherwiththeirtextualdescriptions.Severalotherworks (e.g.[2])treatthefew-exampleprobleminthesamewaythattheydealwith eventdetectionwhenmoreexamplesareavailable(e.g.trainingstandardkernel SVMs).Learningvideoeventdetectorsfromafewexamplesisaproblemthat issimulatedintheTRECVIDMEDtask[22]bythe10Exsubtask,whereonly 10positivesamplesareavailablefortraining.
Inthecaseoflearningfromveryfewpositivesamples,itisofhighinterest tofurtherexploitvideosamplesthatdonotexactlymeettherequirementsfor beingcharacterizedastruepositiveexamplesofanevent,butneverthelessare closelyrelatedtoaneventclassandcanbeseenas“related”examplesofit.This issimulatedintheTRECVIDMEDtask[22]bythe“near-miss”videoexamples providedforeachtargeteventclass.Exceptfor[28],noneoftheaboveworks
takesfulladvantageoftheserelatedvideosforlearningfromfewpositivesamples; instead,the“related”samplesareeitherexcludedfromthetrainingprocedure [2, 11],ortheyaremistreatedastruepositiveortruenegativeinstances[7]. Incontrast,in[28]theauthorsexploitrelatedsamplesbyhandlingthemas weightedpositiveornegativeones,applyinganautomaticweightingtechnique duringthetrainingstage.Tothisend,arelevancedegreein(0, 1]isautomatically assignedtoalltherelatedsamples,indicatingthedegreeofrelevanceofthese observationswiththeclasstheyarerelatedto.Itwasshownthatthisweighting resultedinlearningmoreaccurateeventdetectors.
Regardlessofwhethertheaboveworksaddresstheproblemoflearningfrom afewpositiveexamplesorassumethatanabundanceofsuchexamplesisavailable,theyalltreatthetrainingvideorepresentationsasnoise-freeobservationsin theSVMinputspace.Lookingbeyondtheeventdetectionapplications,though, assuminguncertaintyininputundertheSVMparadigmisnotunusualandhas beenshowntoleadtobetterlearning.Lanckrietetal.[18]consideredabinary classificationproblemwherethemeanandcovariancematrixofeachclassare assumedtobeknown.Xuetal.[29, 30]consideredtherobustclassificationproblemforaclassofnon-box-typeduncertaintysets,incontrastto[1, 18, 25],who robustifiedregularizedclassificationusingbox-typeuncertainty.Finally,in[27], Tzelepisetal.proposedalinearmaximum-marginclassifier,calledSVMwith GaussianSampleUncertainty,dealingwithuncertaininputdata.Theuncertaintyin[27]canbemodeledeitherisotropicallyoranisotropically,arrivingat aconvexoptimizationproblemthatissolvedusingagradientdescentapproach. Tothebestofourknowledge,therehasbeennostudydealingwithuncertaintyinthevideoeventdetectionproblem,exceptfor[27].However,[27]introduceslinearclassifiers,whichintheeventdetectionproblemarenotexpectedto performinparwithtraditionalkernelSVMsthataretypicallyused(e.g.[11, 31]), despitetheadvantagesofconsideringdatauncertaintyinthelearningprocess. Inthiswork,weextendtheabovestudyandkernelizetheLSVM-iGSUof[27], undertheassumptionofisotropicsampleuncertainty.Weapplytheresulting KSVM-iGSUtotheeventdetectionproblemwhenonlyafewpositivesamples areavailablefortraining.Moreover,weproposeafurtherextensionofKSVMiGSU,namelyRelevanceDegreeKSVM-iGSU(RD-KSVM-iGSU),inspiredby [28],suchthatrelatedsamplescanalsobeexploitedasweightedpositiveor negativeones,basedonanautomaticweightingscheme.
3KernelSVM-iGSU
3.1OverviewofLSVM-iGSU
LSVM-iGSU[27]isaclassifierthattakesainputtrainingdatathataredescribed notsolelybyasetoffeaturerepresentations,i.e.asetofvectors xi insome n-dimensionalspace,butratherbyasetofmultivariateisotropicGaussiandistributionswhichmodeltheuncertaintyofeachtrainingexample.Thatis,every
trainingdatumischaracterizedbyameanvector xi ∈ Rn andanisotropiccovariancematrix,i.e.ascalarmultipleoftheidentitymatrix,Σi = σ 2 i In ∈ Sn ++ 1 . LSVM-iGSUisobtainedbyminimizing,withrespectto w , b,theobjective function J : Rn × R → R givenby
where l isthenumberoftrainingdata, w · x + b =0denotestheseparating hyperplane,andtheloss L :(Rn × R) × (Rn × Sn ++ ×{±1}) → R isgivenby
where xi and σ 2 i In denotethemeanvectorandthecovariancematrixofthe i-th inputentity(Gaussiandistribution),respectively, yi denotesitsground-truth label,anderf(x)= 2 √π x 0 e t2 dt denotestheerrorfunction.
Asdiscussedin[27],(1)isconvexandthusa(global)optimalsolution(w ,b) canbeobtainedusingagradientdescentalgorithm.Theresulting(linear)decisionfunction f (x)= w · x + b isusedinthetestingphaseforclassifyinganunseen samplesimilarlytothestandardlinearSVMalgorithm[4];thatis,accordingto thedistancebetweenthetestingsampleandtheseparatinghyperplane,without takingintoaccountanyuncertaintyestimatesthatcouldbemadeforthetesting samplerepresentation.
3.2KernelizingLSVM-iGSU(KSVM-iGSU)
Theoptimizationproblemdiscussedintheprevioussectioncanberecasted asavariationalcalculusproblemoffindingthefunction f thatminimizesthe functionalΦ[f ]:
wherethefunctionalΦ[f ]isgivenby
1 Sn ++ denotestheconvexconeofallsymmetricpositivedefinite n × n matriceswith entriesin R In denotestheidentitymatrixoforder n
where λ =1/C isaregularizationparameterand f belongstoaReproducing KernelHilbertSpace(RKHS), H,withassociatedkernel k .Usingageneralized semi-parametricversion[24]oftherepresentertheorem[17],itcanbeshown thattheminimizeroftheabovefunctionaladmitsasolutionoftheform
where b ∈ R, αi ∈ R, ∀i. Usingthereproducingproperty,wehave
2 H = f,f H =
,
where K isthekernelmatrix,i.e.thesymmetricpositivedefinite l × l matrix definedas K =(k (xi , xj ))l i,j =1 ,and α =(α1 , ··· ,αl ) .Moreover,weobserve that f (xi )= l j =1 αj k (xi , xj )= Ki · α,where Ki denotesthe i-thcolumnof thekernelmatrix K .Then,theobjectivefunction JH : Rl × R → R isgivenby
wheretheabovesumgivesthetotalloss.We(jointly)minimizetheaboveconvex2 objectivefunctionwithrespectto α, b similarlyto[27]usingtheLimitedmemoryBFGS(L-BFGS)algorithm[20].L-BFGSisaquasi-NewtonoptimizationalgorithmthatapproximatestheBroyden-Fletcher-Goldfarb-Shanno (BFGS)[3]algorithmusingalimitedamountofcomputermemory.Itrequires thefirstorderderivativesoftheobjectivefunctionwithrespecttotheoptimizationvariables α, b.Theyaregiven3 ,respectively,asfollows
2 ConvexitycanbeshownusingTheorem2provedin[27]. 3 Theirderivationisomitted,asitistechnicalbutstraightforward.
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