Learning Colours from Textures by Sparse Manifold Embedding

  • Jun Li
  • Wei Bian
  • Dacheng Tao
  • Chengqi Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)


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.


Reference Image Image Patch Grayscale Image Sparse Constraint Transfer Colour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jun Li
    • 1
  • Wei Bian
    • 1
  • Dacheng Tao
    • 1
  • Chengqi Zhang
    • 1
  1. 1.Center for Quantum Computation & Intelligent SystemsUniversity of TechnologySydneyAustralia

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