Abstract
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.
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References
Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. In: SIGGRAPH, pp. 689–694 (2004)
Yatziv, L., Sapiro, G.: Fast image video colorization using chrominance blending. IEEE Trans. on Image Proc. 15, 1120–1129 (2006)
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)
Kim, T., Lee, K., Lee, S.: Edge-preserving colorization using data-driven random walks with restart. In: IEEE ICIP, pp. 1661–1664 (2009)
Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. ACM Trans. Graph. (TOG) 21, 277–280 (2002)
Ikonen, L., Toivanen, P.: Distance and nearest neighbor transforms on gray-level surfaces. Pattern Rec. Lett. 28, 604–612 (2007)
Heu, J., Hyun, D., Kim, C., Lee, S.: Image and video colorization based on prioritized source propagation. In: IEEE ICIP, pp. 465–468 (2009)
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)
Lipowezky, U.: Grayscale aerial and space image colorization using texture classification. Pattern Rec. Lett. 27, 275–286 (2006)
Seber, G.: Multivariate Observations. Wiley, Chichester (1984)
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Kawulok, M., Kawulok, J., Smolka, B. (2011). Textural Features for Scribble-Based Image Colorization. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20320-6_28
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DOI: https://doi.org/10.1007/978-3-642-20320-6_28
Publisher Name: Springer, Berlin, Heidelberg
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