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dc.contributor.authorHarandi, Mehrtash
dc.contributor.authorHartley, Richard
dc.contributor.authorShen, Chunhua
dc.contributor.authorLovell, Brian
dc.contributor.authorSanderson, Conrad
dc.date.accessioned2020-07-30T23:38:23Z
dc.date.available2020-07-30T23:38:23Z
dc.date.issued2015
dc.identifier.issn0920-5691
dc.identifier.doi10.1007/s11263-015-0833-x
dc.identifier.urihttp://hdl.handle.net/10072/395966
dc.description.abstractSparsity-based representations have recently led to notable results in various visual recognition tasks. In a separate line of research, Riemannian manifolds have been shown useful for dealing with features and models that do not lie in Euclidean spaces. With the aim of building a bridge between the two realms, we address the problem of sparse coding and dictionary learning in Grassmann manifolds, i.e., the space of linear subspaces. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping. This in turn enables us to extend two sparse coding schemes to Grassmann manifolds. Furthermore, we propose an algorithm for learning a Grassmann dictionary, atom by atom. Lastly, to handle non-linearity in data, we extend the proposed Grassmann sparse coding and dictionary learning algorithms through embedding into higher dimensional Hilbert spaces. Experiments on several classification tasks (gender recognition, gesture classification, scene analysis, face recognition, action recognition and dynamic texture classification) show that the proposed approaches achieve considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelized Affine Hull Method and graph-embedding Grassmann discriminant analysis.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofpagefrom113
dc.relation.ispartofpageto136
dc.relation.ispartofissue2-3
dc.relation.ispartofjournalInternational Journal of Computer Vision
dc.relation.ispartofvolume114
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleExtrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationHarandi, M; Hartley, R; Shen, C; Lovell, B; Sanderson, C, Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds, International Journal of Computer Vision, 2015, 114 (2-3), pp. 113-136
dc.date.updated2020-07-29T04:56:45Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2015 Springer. This is an electronic version of an article published in International Journal of Computer Vision, 2015, 114 (2-3), pp. 113-136. European Journal of Nutrition is available online at: http://link.springer.com/ with the open URL of your article.
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gro.griffith.authorSanderson, Conrad


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