Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The error maps are contrast enhanced for better visibility in print.
References
Mulligan, T., Wooters, D.: Geschichte der Fotografie. Von 1839 bis heute. TASCHEN (2015)
Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. ACM Trans. Graph. 21, 277–280 (2002)
Bugeau, A., Ta, V.T., Papadakis, N.: Variational exemplar-based image colorization. IEEE Trans. Image Process. 23, 298–307 (2014)
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)
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)
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)
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)
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)
Levin, A., Lischinski, D., Y.W.: Colorization using optimization. ACM Trans. Graph. (TOG) (2004). Proceedings of ACM SIGGRAPH
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)
Yatziv, L., Sapiro, G.: Fast image and video colorization using chrominance blending. IEEE Trans. Image Process. 15, 1120–1129 (2006)
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)
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). https://doi.org/10.1007/978-3-540-88690-7_10
Liu, X., et al.: Intrinsic colorization. ACM Trans. Graph. 27 (2008)
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)
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)
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). https://doi.org/10.1007/978-3-319-46487-9_40
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). https://doi.org/10.1007/978-3-319-71249-9_10
Breiman, L.: Random forests. Statistics Department University of California Berkeley, CA, 94720 (2001)
Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1465–1479 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Mohn, H., Gaebelein, M., Hänsch, R., Hellwich, O. (2019). Random Forests Based Image Colorization. In: Bechmann, D., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2018. Communications in Computer and Information Science, vol 997. Springer, Cham. https://doi.org/10.1007/978-3-030-26756-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-26756-8_14
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)