Genetic Algorithms for Training Data and Polynomial Optimization in Colorimetric Characterization of Scanners

  • Leonardo Vanneschi
  • Mauro Castelli
  • Simone Bianco
  • Raimondo Schettini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)


Generalization is an important issue in colorimetric characterization of devices. We propose a framework based on Genetic Algorithms to select training samples from large datasets. Even though the framework is general, and can be used in principle for any dataset, we use two well known datasets as case studies: training samples are selected from the Macbeth ColorCheckerDC dataset and the trained models are tested on the Kodak Q60 photographic standard dataset. The presented experimental results show that the proposed framework has better, or at least comparable, performances than a set of other computational methods defined so far for the same goal (Hardeberg, Cheung, CIC and Schettini). Even more importantly, the proposed framework has the ability to optimize the training samples and the characterizing polynomial’s coefficients at the same time.


Genetic Algorithm Training Sample Degree Polynomial Color Patch Polynomial Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bianco, S., Gasparini, F., Schettini, R., Vanneschi, L.: An evolutionary framework for colorimetric characterization of scanners. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., McCormack, J., O’Neill, M., Romero, J., Rothlauf, F., Squillero, G., Uyar, A.Ş., Yang, S. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 245–254. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Bianco, S., Gasparini, F., Schettini, R., Vanneschi, L.: Polynomial modeling and optimization for colorimetric characterization of scanners. Journal of Electronic Imaging 17(4), 1–13 (2008)CrossRefGoogle Scholar
  3. 3.
    Cheung, T.L., Westland, S.: Color selections for characterization charts. In: Proceedings of the Second European Conference on Colour in Graphics, Imaging and Vision, Aachen, Germany, pp. 116–119 (2004)Google Scholar
  4. 4.
    Cheung, T., Westland, S., Connah, D., Ripamonti, C.: Characterization of colour cameras using neural networks and polynomial transforms. Journal of Coloration Technology 120(1), 19–25 (2004)CrossRefGoogle Scholar
  5. 5.
    Cheung, V., Westland, S., Li, C., Hardeberg, J., Connah, D.: Characterization of trichromatic color cameras by using a new multispectral imaging technique. J. Opt. Soc. Am. A 22, 1231–1240 (2005)CrossRefGoogle Scholar
  6. 6.
    Chou, Y., Li, C., Luo, M.: A new colour selection method for characterising digital cameras. In: Proceedings of the 17th Color Imaging Conference (2009) (to appear)Google Scholar
  7. 7.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  8. 8.
    Hardeberg, J.Y., Brettel, H., Schmitt, F.J.M.: Spectral characterization of electronic cameras. In: Electronic Imaging: Processing, Printing, and Publishing in Color. SPIE, Zurich (1998)Google Scholar
  9. 9.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  10. 10.
    Huffel, S., Vandewalle, J.: The total least squares problem: computational aspects and analysis. Society for Industrial and Applied Mathematics, Philadelphia (1991)Google Scholar
  11. 11.
    Kang, H.: Computational coolor technology, vol. PM159. SPIE Press (2006)Google Scholar
  12. 12.
    Pellegri, P., Novati, G., Schettini, R.: Training set selection for multispectral imaging systems characterization. Journal of Imaging Science and Technology 48(3), 203–210 (2004)Google Scholar
  13. 13.
    Shen, H.L., Mou, T.S., Xin, J.: Colorimetric characterization of scanners by measures of perceptual color error. Journal of Electronic Imaging 15(4), 1–5 (2006)CrossRefGoogle Scholar
  14. 14.
    Shen, H.L., Xin, J.: Spectral characterization of a color scanner by adaptive estimation. Journal of the Optical Society of America A 21(7), 1125–1130 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Leonardo Vanneschi
    • 1
  • Mauro Castelli
    • 1
  • Simone Bianco
    • 1
  • Raimondo Schettini
    • 1
  1. 1.Department of Informatics, Systems and Communication (D.I.S.Co.)University of Milano-BicoccaMilanItaly

Personalised recommendations