Skip to main content

Random Forests Based Image Colorization

  • 430 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 997)


The task of image colorization, i.e. assigninging color values to grayscale images, is usually addressed by either exploiting explicit user input or very large training data sets. In contrast, the proposed method is fully automatic and uses several orders of magnitude less training images. To this aim, a Random Forest is tailored to the task of regressing plausible color value given a patch of the grayscale image. In order to improve the colorization performance, the Random Forests also includes a simple position prior. The proposed approach leads to satisfying results over various colorization tasks and compares favorably with the state of the art based on convolutional networks.


  • Image colorization
  • Random forests
  • Regression

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-26756-8_14
  • Chapter length: 17 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-26756-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Fig. 1.

Image taken from our previous work [6] with identical general workflow.

Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.


  1. 1.

    The error maps are contrast enhanced for better visibility in print.


  1. Mulligan, T., Wooters, D.: Geschichte der Fotografie. Von 1839 bis heute. TASCHEN (2015)

    Google Scholar 

  2. Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. ACM Trans. Graph. 21, 277–280 (2002)

    CrossRef  Google Scholar 

  3. Bugeau, A., Ta, V.T., Papadakis, N.: Variational exemplar-based image colorization. IEEE Trans. Image Process. 23, 298–307 (2014)

    CrossRef  MathSciNet  Google Scholar 

  4. Horiuchi, T.: Estimation of color for gray-level image by probabilistic relaxation. In: Object Recognition Supported by User Interaction for Service Robots, vol. 3, pp. 867–870 (2002)

    Google Scholar 

  5. de Queiroz, R.L., Braun, K.M.: Color to gray and back: color embedding into textured gray images. IEEE Trans. Image Process. 15, 1464–1470 (2006)

    CrossRef  Google Scholar 

  6. Mohn, H., Gaebelein, M., Hänsch, R., Hellwich, O.: Towards image colorization with random forests. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol. 4, pp. 270–278. VISAPP, INSTICC, SciTePress (2018)

    Google Scholar 

  7. Karthikeyani, V., Duraiswamy, D.K., Kamalakkannan, P.: Conversion of gray-scale image to color image with and without texture synthesis. Int. J. Comput. Sci. Netw. Secur. 7, 11–16 (2007)

    Google Scholar 

  8. Irony, R., Cohen-Or, D., Lischinski, D.: Colorization by example. In: Proceedings of the Sixteenth Eurographics Conference on Rendering Techniques, EGSR 2005, pp. 201–210 (2005)

    Google Scholar 

  9. Levin, A., Lischinski, D., Y.W.: Colorization using optimization. ACM Trans. Graph. (TOG) (2004). Proceedings of ACM SIGGRAPH

    Google Scholar 

  10. Huang, Y.C., Tung, Y.S., Chen, J.C., Wang, S.W., Wu, J.L.: An adaptive edge detection based colorization algorithm and its applications. In: Proceedings of the 13th Annual ACM International Conference on Multimedia, MULTIMEDIA 2005, pp. 351–354 (2005)

    Google Scholar 

  11. Yatziv, L., Sapiro, G.: Fast image and video colorization using chrominance blending. IEEE Trans. Image Process. 15, 1120–1129 (2006)

    CrossRef  Google Scholar 

  12. Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2001, pp. 327–340 (2001)

    Google Scholar 

  13. Charpiat, G., Hofmann, M., Schölkopf, B.: Automatic image colorization via multimodal predictions. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 126–139. Springer, Heidelberg (2008).

    CrossRef  Google Scholar 

  14. Liu, X., et al.: Intrinsic colorization. ACM Trans. Graph. 27 (2008)

    CrossRef  Google Scholar 

  15. Gupta, R.K., Chia, A.Y.S., Rajan, D., Ng, E.S., Zhiyong, H.: Image colorization using similar images. In: Proceedings of the 20th ACM International Conference on Multimedia, MM 2012, pp. 369–378 (2012)

    Google Scholar 

  16. Cheng, Z., Yang, Q., Sheng, B.: Deep colorization. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), ICCV 2015, pp. 415–423 (2015)

    Google Scholar 

  17. Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 649–666. Springer, Cham (2016).

    CrossRef  Google Scholar 

  18. Cao, Y., Zhou, Z., Zhang, W., Yu, Y.: Unsupervised diverse colorization via generative adversarial networks. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 151–166. Springer, Cham (2017).

    CrossRef  Google Scholar 

  19. Breiman, L.: Random forests. Statistics Department University of California Berkeley, CA, 94720 (2001)

    Google Scholar 

  20. Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1465–1479 (2006)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ronny Hänsch .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Mohn, H., Gaebelein, M., Hänsch, R., Hellwich, O. (2019). Random Forests Based Image Colorization. In: , et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2018. Communications in Computer and Information Science, vol 997. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26755-1

  • Online ISBN: 978-3-030-26756-8

  • eBook Packages: Computer ScienceComputer Science (R0)