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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M9LKLL
Repositorysid.inpe.br/sibgrapi/2016/08.16.18.30
Last Update2016:08.16.18.30.22 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/08.16.18.30.22
Metadata Last Update2022:05.18.22.21.08 (UTC) administrator
Citation KeyFilisbinoGiraThom:2016:TeFiMu
TitleTensor Fields for Multilinear Image Representation and Statistical Learning Models Applications
FormatOn-line
Year2016
Access Date2024, May 03
Number of Files1
Size1868 KiB
2. Context
Author1 Filisbino, Tiene André
2 Giraldi, Gilson Antonio
3 Thomaz, Carlos Eduardo
Affiliation1 National Laboratory for Scientific Computing
2 National Laboratory for Scientific Computing
3 Department of Electrical Engineering, FEI
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addressgilson@lncc.br
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeTutorial
History (UTC)2016-08-16 18:30:22 :: gilson@lncc.br -> administrator ::
2022-05-18 22:21:08 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsTensor Fields
Dimensionality Reduction
Tensor Subspace Learning
Ranking Tensor Components
Reconstruction
MPCA
Face Image Analysis
AbstractNowadays, higher order tensors have been applied to model multidimensional image data for subsequent tensor decomposition, dimensionality reduction and classification tasks. In this paper, we survey recent results with the goal of highlighting the power of tensor methods as a general technique for data representation, their advantage if compared with vector counterparts and some research challenges. Hence, we firstly review the geometric theory behind tensor fields and their algebraic representation. Afterwards, subspace learning, dimensionality reduction, discriminant analysis and reconstruction problems are considered following the traditional viewpoint for tensor fields in image processing, based on generalized matrices. We show several experimental results to point out the effectiveness of multilinear algorithms for dimensionality reduction combined with discriminant techniques for selecting tensor components for face image analysis, considering gender classification as well as reconstruction problems. Then, we return to the geometric approach for tensors and discuss opened issues in this area related to manifold learning and tensor fields, incorporation of prior information and high performance computational requirements. Finally, we offer conclusions and final remarks.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2016 > Tensor Fields for...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3M9LKLL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M9LKLL
Languageen
Target FileSurvey-Paper-Tutorial-Sib-19-07-2016.pdf
User Groupgilson@lncc.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 5
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark 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 versiontype volume


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