Learning Colours from Textures by Sparse Manifold Embedding

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dc.contributor.author Li, J
dc.contributor.author Bian, W
dc.contributor.author Tao, D
dc.contributor.author Zhang, C
dc.contributor.editor Wang, D
dc.contributor.editor Reynolds, M
dc.date.accessioned 2012-10-12T03:36:16Z
dc.date.issued 2011-01
dc.identifier.citation Lecture Notes in Artificial Intelligence.AI 2011: Advances in Artificial Intelligence.24th Australasian Joint Conference, 2011, pp. 600 - 608
dc.identifier.isbn 978-3-642-25831-2
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.
dc.format Scott McWhirter
dc.publisher Springer-Verlag Berlin / Heidelberg
dc.relation.isbasedon 10.1007/978-3-642-25832-9_61
dc.title Learning Colours from Textures by Sparse Manifold Embedding
dc.type Conference Proceeding
dc.parent Lecture Notes in Artificial Intelligence.AI 2011: Advances in Artificial Intelligence.24th Australasian Joint Conference
dc.journal.number en_US
dc.publocation Berlin/Heidelberg en_US
dc.publocation Berlin/Heidelberg
dc.publocation Berlin/Heidelberg
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.conference AI 2011: Advances in Artificial Intelligence.24th Australasian Joint Conference
dc.conference AI 2011: Advances in Artificial Intelligence.24th Australasian Joint Conference
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 011221
dc.personcode 111727
dc.personcode 115849
dc.personcode 111502
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.date.activity 2011-12-05
dc.date.activity 2011-12-05
dc.location.activity Perth, Australia en_US
dc.location.activity Perth, Australia
dc.location.activity Perth, Australia
dc.description.keywords NA en_US
dc.description.keywords NA
dc.description.keywords NA
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10


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