Textural Features for Scribble-Based Image Colorization

  • Michal Kawulok
  • Jolanta Kawulok
  • Bogdan Smolka
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)


In this paper we propose how to exploit image textural features to improve scribble-based image colorization. The existing techniques work by propagating color from the user-added scribbles over the whole image. The color propagation paths are determined so as to minimize the luminance changes integrated along the path. In our method, at first linear discriminant analysis is performed over the scribble pixels to extract discriminative textural features (DTF). Our contribution to image colorization lies in using DTF for the path optimization instead of the luminance. The colorization results presented in the paper explain and confirm the method’s robustness compared with the alternative existing techniques.


Textural Feature Linear Discriminant Analysis Propagation Path Local Binary Pattern Color Propagation 
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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  1. 1.
    Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. In: SIGGRAPH, pp. 689–694 (2004)Google Scholar
  2. 2.
    Yatziv, L., Sapiro, G.: Fast image video colorization using chrominance blending. IEEE Trans. on Image Proc. 15, 1120–1129 (2006)CrossRefGoogle Scholar
  3. 3.
    Lagodzinski, P., Smolka, B.: Digital image colorization based on probabilistic distance transform. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 626–634. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Kim, T., Lee, K., Lee, S.: Edge-preserving colorization using data-driven random walks with restart. In: IEEE ICIP, pp. 1661–1664 (2009)Google Scholar
  5. 5.
    Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. ACM Trans. Graph. (TOG) 21, 277–280 (2002)CrossRefGoogle Scholar
  6. 6.
    Ikonen, L., Toivanen, P.: Distance and nearest neighbor transforms on gray-level surfaces. Pattern Rec. Lett. 28, 604–612 (2007)CrossRefGoogle Scholar
  7. 7.
    Heu, J., Hyun, D., Kim, C., Lee, S.: Image and video colorization based on prioritized source propagation. In: IEEE ICIP, pp. 465–468 (2009)Google Scholar
  8. 8.
    Zhang, J., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. of Computer Vision 73, 213–238 (2007)CrossRefGoogle Scholar
  9. 9.
    Lipowezky, U.: Grayscale aerial and space image colorization using texture classification. Pattern Rec. Lett. 27, 275–286 (2006)CrossRefGoogle Scholar
  10. 10.
    Seber, G.: Multivariate Observations. Wiley, Chichester (1984)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michal Kawulok
    • 1
  • Jolanta Kawulok
    • 1
  • Bogdan Smolka
    • 1
  1. 1.Faculty of Automatic Control, Electronics and Computer ScienceSilesian University of TechnologyGliwicePoland

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