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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier6qtX3pFwXQZG2LgkFdY/LPfUC
Repositorysid.inpe.br/sibgrapi@80/2006/07.20.16.38
Last Update2006:07.20.16.38.30 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi@80/2006/07.20.16.38.31
Metadata Last Update2022:06.14.00.13.16 (UTC) administrator
DOI10.1109/SIBGRAPI.2006.48
Citation KeyCámaraChávezCorPrePhiAlb:2006:ViSeSu
TitleVideo Segmentation by Supervised Learning
FormatOn-line
Year2006
Access Date2023, Dec. 10
Number of Files1
Size208 KiB
2. Context
Author1 Cámara Chávez, Guillermo
2 Cord, Matthieu
3 Precioso, Frederic
4 Philipp-Foliguet, Sylvie
5 de Albuquerque Araújo, Arnaldo
Affiliation1 Equipe Traiment des Images et du Signal - ENSEA
2 Equipe Traiment des Images et du Signal - ENSEA
3 Equipe Traiment des Images et du Signal - ENSEA
4 Equipe Traiment des Images et du Signal - ENSEA
5 Departamento de Ciência da Computação - UFMG
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
e-Mail Addresschavez@ensea.fr
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Conference LocationManaus, AM, Brazil
Date8-11 Oct. 2006
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2006-07-20 16:38:31 :: gcamarac -> banon ::
2006-08-30 21:49:58 :: banon -> gcamarac ::
2008-07-17 14:11:03 :: gcamarac -> administrator ::
2009-08-13 20:38:09 :: administrator -> banon ::
2010-08-28 20:02:24 :: banon -> administrator ::
2022-06-14 00:13:16 :: administrator -> :: 2006
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsvideo segmentation
cut detection
supervised learning
AbstractIn most of video shot boundary detection algorithms, proposed in the literature, several parameters and thresholds have to be set in order to achieve good results. In this paper, to get rid of parameters and thresholds, we explore a supervised classification method for video shot segmentation. We transform the temporal segmentation into a class categorization issue. Our approach defines a uniform framework for combining different kinds of features extracted from the video. Our method does not require any pre-processing step to compensate motion or post-processing filtering to eliminate false detected transitions. The experiments, following strictly the TRECVID 2002 competition protocol, provide very good results dealing with a large amount of features thanks to our kernel-based SVM classification method.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2006 > Video Segmentation by...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Video Segmentation by...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/LPfUC
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZG2LgkFdY/LPfUC
Languageen
Target Filesibgrapi_camara_video.pdf
User Groupgcamarac
administrator
Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46RFT7E
8JMKD3MGPEW34M/4742MCS
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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