Learning Colours from Textures by Sparse Manifold Embedding

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dc.contributor.author Li, Jun en_US
dc.contributor.author Bian, Wei en_US
dc.contributor.author Tao, Dacheng en_US
dc.contributor.author Zhang, Chengqi en_US
dc.contributor.editor Dianhui Wang, Mark Reynolds en_US
dc.date.accessioned 2012-10-12T03:36:16Z
dc.date.available 2012-10-12T03:36:16Z
dc.date.issued 2011 en_US
dc.identifier 2011001468 en_US
dc.identifier.citation Li Jun et al. 2011, 'Learning Colours from Textures by Sparse Manifold Embedding', , Springer-Verlag Berlin / Heidelberg, Berlin/Heidelberg, , pp. 600-608. en_US
dc.identifier.issn 978-3-642-25831-2 en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/19127
dc.description.abstract The capability of inferring colours from the texture (grayscale contents) of an image is useful in many application areas, when the imaging device/environment is limited. Traditional colour assignment involves intensive human effort. Automatic methods have been proposed to establish relations between image textures and the corresponding colours. Existing research mainly focuses on linear relations. In this paper, we employ sparse constraints in the model of texture-colour relationship. The technique is developed on a locally linear model, which assumes manifold assumption of the distribution of the image data. Given the texture of an image patch, learning the model transfers colours to the texture patch by combining known colours of similar texture patches. The sparse constraint checks the contributing factors in the model and helps improve the stability of the colour transfer. Experiments show that our method gives superior results to those of the previous work. en_US
dc.language English en_US
dc.publisher Springer-Verlag Berlin / Heidelberg en_US
dc.relation.isbasedon http://dx.doi.org/10.1007/978-3-642-25832-9_61 en_US
dc.title Learning Colours from Textures by Sparse Manifold Embedding en_US
dc.parent Lecture Notes in Artificial Intelligence.AI 2011: Advances in Artificial Intelligence.24th Australasian Joint Conference en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Berlin/Heidelberg en_US
dc.identifier.startpage 600 en_US
dc.identifier.endpage 608 en_US
dc.cauo.name FEIT.A/DRsch Ctr Quantum Computat'n & Intelligent Systs en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 111727 en_US
dc.personcode 115849 en_US
dc.personcode 111502 en_US
dc.personcode 011221 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom AI 2011: Advances in Artificial Intelligence.24th Australasian Joint Conference en_US
dc.date.activity 20111205 en_US
dc.location.activity Perth, Australia en_US
dc.description.keywords NA en_US
dc.staffid en_US
dc.staffid 011221 en_US

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