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dc.contributor.authorHarandi, Mehrtash
dc.contributor.authorSanderson, Conrad
dc.contributor.authorHartley, Richard
dc.contributor.authorLovell, Brian
dc.date.accessioned2021-01-12T22:48:13Z
dc.date.available2021-01-12T22:48:13Z
dc.date.issued2012
dc.identifier.isbn9783642337086
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-642-33709-3_16
dc.identifier.urihttp://hdl.handle.net/10072/400955
dc.description.abstractRecent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry. This paper tackles the problem of sparse coding and dictionary learning in the space of symmetric positive definite matrices, which form a Riemannian manifold. With the aid of the recently introduced Stein kernel (related to a symmetric version of Bregman matrix divergence), we propose to perform sparse coding by embedding Riemannian manifolds into reproducing kernel Hilbert spaces. This leads to a convex and kernel version of the Lasso problem, which can be solved efficiently. We furthermore propose an algorithm for learning a Riemannian dictionary (used for sparse coding), closely tied to the Stein kernel. Experiments on several classification tasks (face recognition, texture classification, person re-identification) show that the proposed sparse coding approach achieves notable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as tensor sparse coding, Riemannian locality preserving projection, and symmetry-driven accumulation of local features.
dc.publisherSpringer
dc.relation.ispartofconferencenameEuropean Conference on Computer Vision
dc.relation.ispartofconferencetitleLecture Notes in Computer Science
dc.relation.ispartofdatefrom2012-10-07
dc.relation.ispartofdateto2012-10-13
dc.relation.ispartoflocationFlorence, Italy
dc.relation.ispartofpagefrom216
dc.relation.ispartofpageto229
dc.relation.ispartofvolume7573
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchKnowledge Representation and Machine Learning
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode080104
dc.subject.fieldofresearchcode170203
dc.titleSparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
dc.typeConference output
dcterms.bibliographicCitationHarandi, M; Sanderson, C; Hartley, R; Lovell, B, Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach, 2012, 7573, pp. 216-229
dc.date.updated2021-01-12T22:44:42Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorSanderson, Conrad


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