Learning colours from textures by sparse manifold embedding

Authors:
Jun Li;Wei Bian;Dacheng Tao;Chengqi Zhang
Affiliations:
Center for Quantum Computation & Intelligent Systems, University of Technology, Sydney, Australia;Center for Quantum Computation & Intelligent Systems, University of Technology, Sydney, Australia;Center for Quantum Computation & Intelligent Systems, University of Technology, Sydney, Australia;Center for Quantum Computation & Intelligent Systems, University of Technology, Sydney, Australia
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

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Learning colours from textures by sparse manifold embedding

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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.