Abstract
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.
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References
Beymer, D., Poggio, T.: Image representation for visual learning. Science (1996)
Chang, H., Yeung, D.Y., **ong, Y.: Super-resolution through neighbor embedding. In: Proceedings of CVPR (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of CVPR (2005)
Donoho, D.L., Grimes, C.: Image manifolds which are isometric to euclidean space. J. Math. Imaging Vis. 23(1), 5–24 (2005)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Annals of Statistics 32(2), 407–451 (2004)
Fan, W., Yeung, D.-Y.: Image hallucination using neighbor embedding over visual primitive manifolds. In: Proceedings of CVPR (2007)
Huo, X., Ni, X., Smith, A.K.: A survey of manifold-based learning methods. Technical report, Statistics Group, Georgia Institute of Technology (2006)
Li, J., Hao, P.: Transferring colours to grayscale images by locally linear embedding. In: BMVC (2008)
Qu, Y., Wong, T.-T., Heng, P.-A.: Manga colorization. In: Proceedings of Siggraph (2006)
Roweis, S.T., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science (2000)
Seung, H.S., Lee, D.D.: The manifold way of perception. Science, 2268–2269 (2000)
Silberg, J.: Cinesite press article (1998), http://www.cinesite.com/core/press/articles/1998/10_00_98-team.html
Song, M., Tao, D., Chen, C., Li, X., Chen, C.W.: Color to gray: Visual cue preservation. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 1537–1552 (2010)
Souvenir, R.: Manifold learning for natural image sets. Ph.D. thesis (2006)
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological), 267–288 (1996)
Verbeek, J.: Learning non-linear image manifolds by combining local linear models. IEEE Transactions on Pattern Analysis & Machine Intelligence 28(8), 1236–1250 (2006)
Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. In: Proceedings of Siggraph (2002)
Zhang, T., Tao, D., Li, X., Yang, J.: Patch alignment for dimensionality reduction. IEEE Trans. Knowl. Data Eng. 21(9), 1299–1313 (2009)
Zou, H., Hastie, T., Tibshirani, R.: Sparse principal component analysis. Jcgs 15(2), 262–286 (2006)
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Li, J., Bian, W., Tao, D., Zhang, C. (2011). Learning Colours from Textures by Sparse Manifold Embedding. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_61
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DOI: https://doi.org/10.1007/978-3-642-25832-9_61
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