Annotating News Video with Locations
Identifieur interne : 000A43 ( Istex/Corpus ); précédent : 000A42; suivant : 000A44Annotating News Video with Locations
Auteurs : Jun Yang ; G. HauptmannSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: The location of video scenes is an important semantic descriptor especially for broadcast news video. In this paper, we propose a learning-based approach to annotate shots of news video with locations extracted from video transcript, based on features from multiple video modalities including syntactic structure of transcript sentences, speaker identity, temporal video structure, and so on. Machine learning algorithms are adopted to combine multi-modal features to solve two sub-problems: (1) whether the location of a video shot is mentioned in the transcript, and if so, (2) among many locations in the transcript, which are correct one(s) for this shot. Experiments on TRECVID dataset demonstrate that our approach achieves approximately 85% accuracy in correctly labeling the location of any shot in news video.
Url:
DOI: 10.1007/11788034_16
Links to Exploration step
ISTEX:77A5C13580350959F2D042E5E5E909FEF27860F6Le document en format XML
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In this paper, we propose a learning-based approach to annotate shots of news video with locations extracted from video transcript, based on features from multiple video modalities including syntactic structure of transcript sentences, speaker identity, temporal video structure, and so on. Machine learning algorithms are adopted to combine multi-modal features to solve two sub-problems: (1) whether the location of a video shot is mentioned in the transcript, and if so, (2) among many locations in the transcript, which are correct one(s) for this shot. 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Proceedings</BookSubTitle><BookVolumeNumber>4071</BookVolumeNumber><BookSequenceNumber>4071</BookSequenceNumber><BookDOI>10.1007/11788034</BookDOI><BookTitleID>139965</BookTitleID><BookPrintISBN>978-3-540-36018-6</BookPrintISBN><BookElectronicISBN>978-3-540-36019-3</BookElectronicISBN><BookChapterCount>65</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2006</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I18032" Priority="1" Type="Secondary">Information Storage and Retrieval</BookSubject><BookSubject Code="I18024" Priority="2" Type="Secondary">Database Management</BookSubject><BookSubject Code="I18040" Priority="3" Type="Secondary">Information Systems Applications (incl.Internet)</BookSubject><BookSubject Code="I18059" Priority="4" Type="Secondary">Multimedia Information Systems</BookSubject><BookSubject Code="I22021" Priority="5" Type="Secondary">Image Processing and Computer Vision</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Hari</GivenName><FamilyName>Sundaram</FamilyName></EditorName><Contact><Email>hari.sundaram@asu.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Milind</GivenName><FamilyName>Naphade</FamilyName></EditorName><Contact><Email>naphade@us.ibm.com</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>John</GivenName><GivenName>R.</GivenName><FamilyName>Smith</FamilyName></EditorName><Contact><Email>jrsmith@watson.ibm.com</Email></Contact></Editor><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Yong</GivenName><FamilyName>Rui</FamilyName></EditorName><Contact><Email>yongrui@microsoft.com</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Arts, Media and Engineering Program</OrgDivision><OrgName>Arizona State University</OrgName><OrgAddress><Postcode>85281</Postcode><City>Tempe</City><State>AZ</State><Country> </Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>Intelligent Information Management Department</OrgDivision><OrgName>IBM T.J. Watson Research Center</OrgName><OrgAddress><Street>19 Skyline Drive</Street><Postcode>10532</Postcode><City>Hawthorne</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>Intelligent Information Management Department</OrgDivision><OrgName>IBM T. J. Watson Research Center</OrgName><OrgAddress><Street>19 Skyline Drive</Street><Postcode>10532</Postcode><City>Hawthorne</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgDivision>Microsoft Corporation</OrgDivision><OrgName>Microsoft China R&D Group</OrgName><OrgAddress><Street>49 Zhichun Road</Street><Postcode>100080</Postcode><City>Beijing</City><Country>China</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part6"><PartInfo TocLevels="0"><PartID>6</PartID><PartSequenceNumber>6</PartSequenceNumber><PartTitle>Session O6: Machine Tagging</PartTitle><PartChapterCount>3</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Image and Video Retrieval</BookTitle></PartContext></PartInfo><Chapter ID="Chap16" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>16</ChapterID><ChapterDOI>10.1007/11788034_16</ChapterDOI><ChapterSequenceNumber>16</ChapterSequenceNumber><ChapterTitle Language="En">Annotating News Video with Locations</ChapterTitle><ChapterFirstPage>153</ChapterFirstPage><ChapterLastPage>162</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2006</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>6</PartID><BookID>978-3-540-36019-3</BookID><BookTitle>Image and Video Retrieval</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Jun</GivenName><FamilyName>Yang</FamilyName></AuthorName><Contact><Email>juny@cs.cmu.edu</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Alexander</GivenName><GivenName>G.</GivenName><FamilyName>Hauptmann</FamilyName></AuthorName><Contact><Email>alex@cs.cmu.edu</Email></Contact></Author><Affiliation ID="Aff20"><OrgDivision>School of Computer Science</OrgDivision><OrgName>Carnegie Mellon University</OrgName><OrgAddress><Street>5000 Forbes Ave.</Street><City>Pittsburgh</City><State>PA</State><Postcode>15213</Postcode><Country>USA</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>The location of video scenes is an important semantic descriptor especially for broadcast news video. In this paper, we propose a learning-based approach to annotate shots of news video with locations extracted from video transcript, based on features from multiple video modalities including syntactic structure of transcript sentences, speaker identity, temporal video structure, and so on. Machine learning algorithms are adopted to combine multi-modal features to solve two sub-problems: (1) whether the location of a video shot is mentioned in the transcript, and if so, (2) among many locations in the transcript, which are correct one(s) for this shot. Experiments on TRECVID dataset demonstrate that our approach achieves approximately 85% accuracy in correctly labeling the location of any shot in news video.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Annotating News Video with Locations</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Annotating News Video with Locations</title></titleInfo><name type="personal"><namePart type="given">Jun</namePart><namePart type="family">Yang</namePart><affiliation>School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., 15213, Pittsburgh, PA, USA</affiliation><affiliation>E-mail: juny@cs.cmu.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Alexander</namePart><namePart type="given">G.</namePart><namePart type="family">Hauptmann</namePart><affiliation>School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., 15213, Pittsburgh, PA, USA</affiliation><affiliation>E-mail: alex@cs.cmu.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2006</dateIssued><copyrightDate encoding="w3cdtf">2006</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: The location of video scenes is an important semantic descriptor especially for broadcast news video. In this paper, we propose a learning-based approach to annotate shots of news video with locations extracted from video transcript, based on features from multiple video modalities including syntactic structure of transcript sentences, speaker identity, temporal video structure, and so on. Machine learning algorithms are adopted to combine multi-modal features to solve two sub-problems: (1) whether the location of a video shot is mentioned in the transcript, and if so, (2) among many locations in the transcript, which are correct one(s) for this shot. Experiments on TRECVID dataset demonstrate that our approach achieves approximately 85% accuracy in correctly labeling the location of any shot in news video.</abstract><relatedItem type="host"><titleInfo><title>Image and Video Retrieval</title><subTitle>5th International Conference, CIVR 2006, Tempe, AZ, USA, July 13-15, 2006. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Hari</namePart><namePart type="family">Sundaram</namePart><affiliation>Arts, Media and Engineering Program, Arizona State University, 85281, Tempe, AZ</affiliation><affiliation>E-mail: hari.sundaram@asu.edu</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Milind</namePart><namePart type="family">Naphade</namePart><affiliation>Intelligent Information Management Department, IBM T.J. Watson Research Center, 19 Skyline Drive, 10532, Hawthorne, NY, USA</affiliation><affiliation>E-mail: naphade@us.ibm.com</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">R.</namePart><namePart type="family">Smith</namePart><affiliation>Intelligent Information Management Department, IBM T. J. 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