Abstract
In recent years, extensive work has been done to design algorithms that strive to mimic the robust human vision system which is able to perceive the true colors and discount the illuminant from a scene viewed under light having different spectral compositions (the feature is called “color constancy”). We propose a straightforward approach to the color constancy problem by employing an Interactive Genetic Algorithm [1] (e.g. a Genetic Algorithm [2], [3] guided by the user) that optimizes a well known and robust variant of color constancy algorithm called “gamut mapping” [4]. Results obtained on a set of test images and comparison to various color constancy algorithms, show that our method achieves a good color constancy behavior with no additional knowledge required besides the image that is to be color-corrected, and with minimal assumptions about the scene captured in the image.
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
Preview
Unable to display preview. Download preview PDF.
References
Takagi, H.: Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation. Proceedings of the IEEE 89, 1275–1296 (2001)
Barnard, K., Cardei, V., Funt, B.V.: A Comparison of Computational Color Constancy Algorithms-Part I: Methodology and Experiments with Synthesized Data. IEEE Trans. on Image Processing 11(9), 972–984 (2002)
Finlayson, G., Drew, M.S., Funt, B.V.: Diagonal transforms suffice for color constancy. Proceedings IEEE Int. Conf. on Computer Vision, 164–171 (1993)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)
Back, T., Fogel, D., Michalewicz, Z., Bäck, T. (eds.): Handbook of Evolutionary Computation. Institute of Physics Publishing (1997)
Bäck, T., Hoffmeister, F.: Extended selection mechanisms in Genetic Algorithms. In: Proceedings 4th Int. Conf. Genetic Algorithms, pp. 92–99 (1991)
Miller, B.L., Goldberg, D.E.: Genetic Algorithms, tournament selection and the effects of noise. Complex Systems 9, 193–212 (1996)
Deb, K., Beyer, H.-G.: Self-Adaptation in Real-Parameter Genetic Algorithms with Simulated Binary Crossover. Evolutionary Computation 9(2), 197–221 (2001)
Munteanu, C., Lazarescu, V.: Improving mutation capabilities in a real-coded GA. In: Poli, R., Voigt, H.-M., Cagnoni, S., Corne, D.W., Smith, G.D., Fogarty, T.C. (eds.) EvoIASP 1999 and EuroEcTel 1999. LNCS, vol. 1596, pp. 138–149. Springer, Heidelberg (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Munteanu, C., Rosa, A., Galan, M., Royo, E.R. (2005). Evolutionary Color Constancy Algorithm Based on the Gamut Mapping Paradigm. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2005. EUROCAST 2005. Lecture Notes in Computer Science, vol 3643. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11556985_53
Download citation
DOI: https://doi.org/10.1007/11556985_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29002-5
Online ISBN: 978-3-540-31829-3
eBook Packages: Computer ScienceComputer Science (R0)