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Finding Images with Similar Lighting Conditions in Large Photo Collections

  • Mauricio Díaz
  • Peter Sturm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

When we look at images taken from outdoor scenes, much of the information perceived is due to the ligthing conditions. In these scenes, the solar beams interact with the atmosphere and create a global illumination that determines the way we perceive objets in the world. Lately, exploration of the sky like the main illuminance component has began to be explored in Computer Vision. Some of these studies could be classified like color-based algorithms while some others fall in the physics-based category. However most of them assume that the photometric and geometric camera parameters are constant, or at least, that they could be determined. This work presents a simple and effective method in order to find images with similar lighting conditions. This method is based on a Gaussian mixture model of sky pixels represented by a 3D histogram in the La*b* color space.

Keywords

Color Space Gaussian Mixture Model Query Image Camera Parameter Global Illumination 
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.

References

  1. 1.
    Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., Cohen, M.: Interactive digital photomontage. In: SIGGRAPH 2004, pp. 294–302. ACM, New York (2004)CrossRefGoogle Scholar
  2. 2.
    Bishop, C.M.: Pattern recognition and machine learning (information science and statistics). Springer, Heidelberg (August 2006)CrossRefGoogle Scholar
  3. 3.
    Burt, P.J., Kolczynski, R.J.: Enhanced image capture through fusion. In: ICCV 1993, pp. 173–182 (1993)Google Scholar
  4. 4.
    Committee, C.T.: Spatial distribution of daylight - luminance distributions of various reference skies., Tech. Report CIE-110-1994, Commission Internationale de l’Éclairage, CIE (1994)Google Scholar
  5. 5.
    Committee, C.T.: Colour appearance model for colour management applications, Tech. Report CIE-TC8-01, Commission Internationale de l’Éclairage, CIE (2002)Google Scholar
  6. 6.
    Hays, J., Efros, A.: Scene completion using millions of photographs. In: SIGGRAPH 2007, vol. 26(3,4) (2007)Google Scholar
  7. 7.
    Hershey, J.R., Olsen, P.A.: Approximating the kullback leibler divergence between gaussian mixture models. In: ICASSP 2007, vol. 4, pp. IV–317–IV–320 (2007)Google Scholar
  8. 8.
    Hoiem, D., Efros, A., Hebert, M.: Geometric context from a single image. In: ICCV 2005, pp. 654–661 (2005)Google Scholar
  9. 9.
    Igawa, N., Koga, Y., Matsuzawa, T., Nakamura, H.: Models of sky radiance distribution and sky luminance distribution. Solar Energy 77(2), 137–157 (2004)CrossRefGoogle Scholar
  10. 10.
    Jacobs, N., Roman, N., Pless, R.: Consistent temporal variations in many outdoor scenes. In: CVPR 2007, pp. 1–6 (2007)Google Scholar
  11. 11.
    Lalonde, J., Hoiem, D., Efros, A., Rother, C., Winn, J., Criminisi, A.: Photo clip art. In: SIGGRAPH 2007, vol. 26 (2007)Google Scholar
  12. 12.
    Lalonde, J., Narasimhan, S.G., Efros, A.: What does the sky tell us about the camera? In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 354–367. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Love, R.C.: Surface reflection model estimation from naturally illuminated image sequences, Tech. report, The University of Leeds, Ph.D. thesis (1997)Google Scholar
  14. 14.
    Manduchi, R.: Learning outdoor color classification. IEEE Transactions on PAMI 28(11), 1713–1723 (2006)Google Scholar
  15. 15.
    Perez, R., Seals, R., Michalsky, J.: All-weather model for sky luminance distribution - preliminary configuration and validation. Solar Energy 50(3), 235–245 (1993)CrossRefGoogle Scholar
  16. 16.
    Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. Int. J. Comput. Vision 80(2), 189–210 (2008)CrossRefGoogle Scholar
  17. 17.
    Stumpfel, J., Jones, A., Wenger, A., Tchou, C., Hawkins, T., Debevec, P.: Direct hdr capture of the sun and sky. In: AFRIGRAPH 2004, pp. 145–149 (2004)Google Scholar
  18. 18.
    Sunkavalli, K., Romeiro, F., Matusik, W., Zickler, Y., Pfister, H.: What do color changes reveal about an outdoor scene? In: CVPR 2008, pp. 1–8 (2008)Google Scholar
  19. 19.
    Wilczkowiak, M., Brostow, G.J., Tordoff, B., Cipolla, R.: Hole filling through photomontage. In: BMVC 2005, July 2005, pp. 492–501 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mauricio Díaz
    • 1
  • Peter Sturm
    • 2
  1. 1.Laboratoire Jean KuntzmannSaint Martin d’HèresFrance
  2. 2.INRIA Grenoble Rhône-AlpesMontbonnotFrance

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