Tensor Fields for Multilinear Image Representation and Statistical Learning Models Applications

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dc.contributor.authorFilisbino T. A.
dc.contributor.authorGiraldi G. A.
dc.contributor.authorCarlos E. Thomaz
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-5566-1963
dc.date.accessioned2022-01-12T21:58:15Z
dc.date.available2022-01-12T21:58:15Z
dc.date.issued2017-01-10
dc.description.abstract© 2016 IEEE.Nowadays, higher order tensors have been applied to model multi-dimensional 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 multi-linear 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.
dc.description.firstpage24
dc.description.lastpage37
dc.identifier.citationFILISBINO, T. A.; GIRALDI, G. A.; THOMAZ, C. E. Tensor Fields for Multilinear Image Representation and Statistical Learning Models Applications. Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials, SIBGRAPI-T 2016, p. 24-37, Jan. 2017.
dc.identifier.doi10.1109/SIBGRAPI-T.2016.012
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3849
dc.relation.ispartofProceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials, SIBGRAPI-T 2016
dc.rightsAcesso Restrito
dc.subject.otherlanguageDimensionality Reduction
dc.subject.otherlanguageFace Image Analysis
dc.subject.otherlanguageMPCA
dc.subject.otherlanguageRanking Tensor Components
dc.subject.otherlanguageReconstruction
dc.subject.otherlanguageTensor Fields
dc.subject.otherlanguageTensor Subspace Learning
dc.titleTensor Fields for Multilinear Image Representation and Statistical Learning Models Applications
dc.typeArtigo de evento
fei.scopus.citations0
fei.scopus.eid2-s2.0-85013643283
fei.scopus.subjectDimensionality reduction
fei.scopus.subjectFace image analysis
fei.scopus.subjectMPCA
fei.scopus.subjectTensor components
fei.scopus.subjectTensor fields
fei.scopus.subjectTensor subspace learning
fei.scopus.updated2024-08-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85013643283&origin=inward
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