News Video Retrieval by Learning Multimodal Semantic Information
Identifieur interne : 000470 ( Istex/Corpus ); précédent : 000469; suivant : 000471News Video Retrieval by Learning Multimodal Semantic Information
Auteurs : Hui Yu ; Bolan Su ; Hong Lu ; Xiangyang XueSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: With the explosion of multimedia data especially that of video data, requirement of efficient video retrieval has becoming more and more important. Years of TREC Video Retrieval Evaluation (TRECVID) research gives benchmark for video search task. The video data in TRECVID are mainly news video. In this paper a compound model consisting of several atom search modules, i.e., textual and visual, for news video retrieval is introduced. First, the analysis on query topics helps to improve the performance of video retrieval. Furthermore, the multimodal fusion of all atom search modules ensures to get good performance. Experimental results on TRECVID 2005 and TRECVID 2006 search tasks demonstrate the effectiveness of the proposed method.
Url:
DOI: 10.1007/978-3-540-76414-4_39
Links to Exploration step
ISTEX:1FF488D7D4445217F511A86D0DFEC99626DCBAFELe document en format XML
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Years of TREC Video Retrieval Evaluation (TRECVID) research gives benchmark for video search task. The video data in TRECVID are mainly news video. In this paper a compound model consisting of several atom search modules, i.e., textual and visual, for news video retrieval is introduced. First, the analysis on query topics helps to improve the performance of video retrieval. Furthermore, the multimodal fusion of all atom search modules ensures to get good performance. Experimental results on TRECVID 2005 and TRECVID 2006 search tasks demonstrate the effectiveness of the proposed method.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Hui Yu</name><affiliations><json:string>Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai, China</json:string><json:string>E-mail: 052021193@fudan.edu.cn</json:string></affiliations></json:item><json:item><name>Bolan Su</name><affiliations><json:string>Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai, China</json:string><json:string>E-mail: 0472422@fudan.edu.cn</json:string></affiliations></json:item><json:item><name>Hong Lu</name><affiliations><json:string>Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai, China</json:string><json:string>E-mail: honglu@fudan.edu.cn</json:string></affiliations></json:item><json:item><name>Xiangyang Xue</name><affiliations><json:string>Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai, China</json:string><json:string>E-mail: xyxue@fudan.edu.cn</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: With the explosion of multimedia data especially that of video data, requirement of efficient video retrieval has becoming more and more important. 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DisplayOrder="Western"><GivenName>Bolan</GivenName><FamilyName>Su</FamilyName></AuthorName><Contact><Email>0472422@fudan.edu.cn</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Hong</GivenName><FamilyName>Lu</FamilyName></AuthorName><Contact><Email>honglu@fudan.edu.cn</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Xiangyang</GivenName><FamilyName>Xue</FamilyName></AuthorName><Contact><Email>xyxue@fudan.edu.cn</Email></Contact></Author><Affiliation ID="Aff1"><OrgName>Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai</OrgName><OrgAddress><Country>China</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>With the explosion of multimedia data especially that of video data, requirement of efficient video retrieval has becoming more and more important. Years of TREC Video Retrieval Evaluation (TRECVID) research gives benchmark for video search task. The video data in TRECVID are mainly news video. In this paper a compound model consisting of several atom search modules, i.e., textual and visual, for news video retrieval is introduced. First, the analysis on query topics helps to improve the performance of video retrieval. Furthermore, the multimodal fusion of all atom search modules ensures to get good performance. Experimental results on TRECVID 2005 and TRECVID 2006 search tasks demonstrate the effectiveness of the proposed method.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Video Retrieval</Keyword><Keyword>Rich Semantic Information</Keyword><Keyword>TRECVID</Keyword><Keyword>Manual Search Task</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>News Video Retrieval by Learning Multimodal Semantic Information</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>News Video Retrieval by Learning Multimodal Semantic Information</title></titleInfo><name type="personal"><namePart type="given">Hui</namePart><namePart type="family">Yu</namePart><affiliation>Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai, China</affiliation><affiliation>E-mail: 052021193@fudan.edu.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Bolan</namePart><namePart type="family">Su</namePart><affiliation>Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai, China</affiliation><affiliation>E-mail: 0472422@fudan.edu.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Hong</namePart><namePart type="family">Lu</namePart><affiliation>Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai, China</affiliation><affiliation>E-mail: honglu@fudan.edu.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Xiangyang</namePart><namePart type="family">Xue</namePart><affiliation>Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science & Engineering, Fudan University, Shanghai, China</affiliation><affiliation>E-mail: xyxue@fudan.edu.cn</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">2007</dateIssued><copyrightDate encoding="w3cdtf">2007</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: With the explosion of multimedia data especially that of video data, requirement of efficient video retrieval has becoming more and more important. Years of TREC Video Retrieval Evaluation (TRECVID) research gives benchmark for video search task. The video data in TRECVID are mainly news video. In this paper a compound model consisting of several atom search modules, i.e., textual and visual, for news video retrieval is introduced. First, the analysis on query topics helps to improve the performance of video retrieval. Furthermore, the multimodal fusion of all atom search modules ensures to get good performance. Experimental results on TRECVID 2005 and TRECVID 2006 search tasks demonstrate the effectiveness of the proposed method.</abstract><relatedItem type="host"><titleInfo><title>Advances in Visual Information Systems</title><subTitle>9th International Conference, VISUAL 2007 Shanghai, China, June 28-29, 2007 Revised Selected Papers</subTitle></titleInfo><name type="personal"><namePart type="given">Guoping</namePart><namePart type="family">Qiu</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Clement</namePart><namePart type="family">Leung</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Xiangyang</namePart><namePart type="family">Xue</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Robert</namePart><namePart type="family">Laurini</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2007</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I18032">Information Storage and Retrieval</topic><topic authority="SpringerSubjectCodes" authorityURI="I18030">Data Mining and Knowledge Discovery</topic><topic authority="SpringerSubjectCodes" authorityURI="I18040">Information Systems Applications (incl.Internet)</topic><topic authority="SpringerSubjectCodes" authorityURI="I18024">Database Management</topic><topic authority="SpringerSubjectCodes" authorityURI="I22005">Computer Imaging, Vision, Pattern 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