Learning Photometric Invariance for Object Detection


Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.

Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time.

Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods.

This is a preview of subscription content, access via your institution.


  1. Álvarez, J. M., López, A. M., & Baldrich, R. Illuminant-invarariant model-based road segmentation. In Proceedings of the 2008 IEEE international vehicles symposium (IV’08), Eindhoven, The Netherlands.

  2. Best, P. (1998). Implementing value at risk. New York: Wiley.

    Book  Google Scholar 

  3. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  4. Brown, G., Wyatt, J., Harris, R., & Yao, X. (2005). Diversity creation methods: a survey and categorisation.

  5. Chai, D., & Ngan, K. (1999). Face segmentation using skin-color map in videophone applications. IEEE Transactions on Circuits and Systems for Video Technology, 9(4), 551–564.

    Article  Google Scholar 

  6. Dowd, K. (1998). Beyond value at risk: the new science of risk management. New York: Wiley.

    MATH  Google Scholar 

  7. Finlayson, G. D., Drew, M. S., & Lu, C. (2004). Intrinsic images by entropy minimization. In Proceedings of the European conference on computer vision (ECCV) (Vol. 3, pp. 582–595).

  8. Finlayson, G., Hordley, S., Lu, C., & Drew, M. (2006). On the removal of shadows from images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(1).

  9. Fleck, M. M., Forsyth, D. A., & Bregler, C. (1996). Finding naked people. In Proceedings of the European conference on computer vision (ECCV) (Vol. 3, pp. 593–602). Berlin: Springer

    Google Scholar 

  10. Geusebroek, J. M., Burghouts, G. J., & Smeulders, A. W. M. (2005). The Amsterdam library of object images. International Journal Computer Vision, 61(1), 103–112.

    Article  Google Scholar 

  11. Hartigan, J. A., & Hartigan, P. M. (1985). The dip test of unimodality. The Annals of Statistics, 13(1), 70–84.

    MATH  Article  MathSciNet  Google Scholar 

  12. Ikonomakis, N., Plataniotis, K., & Venetsanopoulos, A. (2000). Color image segmentation for multimedia applications. Journal of Intelligent Robotics Systems, 28(1–2).

  13. Jacobs, R. A. (1995). Methods for combining experts’ probability assessments. Neural Computing, 7(5), 867–888.

    Article  Google Scholar 

  14. Jolliffe, I. T. (2002). Springer series in statistics. Principal component analysis (2nd ed.). Berlin: Springer.

    MATH  Google Scholar 

  15. Jones, M. J., & Rehg, J. M. (2002). Statistical color models with application to skin detection. International Journal Computer Vision, 46(1), 81–96.

    MATH  Article  Google Scholar 

  16. Kender, J. (2005). Saturation, hue and normalized color: calculation, digitation effects, and use (Tech. Rep. CMU-RI-TR-05-40). Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.

  17. Kittler, J., Hatef, M., Duin, R., & Matas, J. (1998). On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), 226–239.

    Article  Google Scholar 

  18. Kovac, J., Peer, P., & Solina, F. (2003). Human skin color clustering for face detection. In International conference on computer as a tool (EUROCON).

  19. Kuncheva, L. I. (2004). Combining pattern classifiers: methods and algorithms. New York: Wiley-Interscience.

    MATH  Book  Google Scholar 

  20. Markowitz, H. M. (1959). Portfolio selection: efficient diversification of investments. New York: Wiley.

    Google Scholar 

  21. Melville, P., & Mooney, R. J. (2005). Creating diversity in ensembles using artificial data. Information Fusion, 6(3), 1553–1563.

    Google Scholar 

  22. Michaud, R. O., & Michaud, R. (2008). Estimation error and portfolio optimization: a resampling solution. Journal of Investment Management, 6(1), 8–28.

    Google Scholar 

  23. Michaud, R. O. (1998). Efficient asset management: a practical guide to stock portfolio optimization and asset allocation. Oxford: Oxford University Press.

    Google Scholar 

  24. Rasmussen, M. (2003). Quantitative portfolio optimisation, asset allocation and risk management (Finance and capital markets). Basingstoke: Palgrave Macmillan.

    Google Scholar 

  25. Ridler, T., & Calvard, S. (1978). Picture thresholding using an iterative selection method. IEEE Transactions on Systems, Man and Cybernetics, 8(8), 630–632.

    Article  Google Scholar 

  26. Rotaru, C., Graf, T., & Zhang, J. (2008). Color image segmentation in hsi space for automotive applications. Journal of Real-Time Image Processing, 1164–1173.

  27. Scherer, B. (2002). Portfolio construction and risk budgeting (Chap. 4). London: Rosk Books.

    Google Scholar 

  28. Sharpe, W. (1994). The sharpe ratio. Journal of Portfolio Management, 21, 49–58.

    Article  Google Scholar 

  29. Sigal, L., Sclaroff, S., & Athitsos, V. (2004). Skin color-based video segmentation under time-varying illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(7), 862–877.

    Article  Google Scholar 

  30. Sobottka, K., & Pitas, I. (1998). A novel method for automatic face segmentation, facial feature extraction and tracking. Signal Processing: Image Communication, 12(3), 263–281.

    Article  Google Scholar 

  31. Sotelo, M., Rodriguez, F., Magdalena, L., Bergasa, L., & Boquete, L. (2004). A color vision-based lane tracking system for autonomous driving in unmarked roads. Autonomous Robots, 16(1).

  32. Stokman, H., & Gevers, T. (2007). Selection and fusion of color models for image feature detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3), 371–381.

    Article  Google Scholar 

  33. Tan, C., Hong, T., Chang, T., & Shneier, M. (2006). Color model-based real-time learning for road following. In Proceedings of the IEEE international conference on intelligent transport systems (pp. 939–944).

  34. Tax, D. M. J., & Duin, R. P. W. (2002). Uniform object generation for optimizing one-class classifiers. Journal of Machine Learning Research, 2, 155–173.

    MATH  Article  Google Scholar 

  35. Tse, Y. K. (1991). Stock returns volatility in the Tokyo stock exchange. Japan and the World Economy, 3(3), 285–298.

    Article  Google Scholar 

  36. Usmen, N. M. H. (2003). Resampled frontiers versus diffuse Bayes: an experiment. Journal of Investment Management, 1(4), 1–17.

    Google Scholar 

  37. van de Sande, K. E. A., Gevers, T., & Snoek, C. G. M. (2008). Evaluation of color descriptors for object and scene recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 453–464).

  38. Weber, M. (1999). The Caltech frontal face dataset. California Inst. of Tech., USA. http://www.vision.caltech.edu/html-files/archive.html. Accessed 1 March 2010.

  39. Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83.

    Article  Google Scholar 

  40. Wyszecki, G., & Stiles, W. (1982). Color science: concepts and methods, quantitative data and formulae (2nd ed.). New York: Wiley.

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Jose M. Álvarez.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Álvarez, J.M., Gevers, T. & López, A.M. Learning Photometric Invariance for Object Detection. Int J Comput Vis 90, 45–61 (2010). https://doi.org/10.1007/s11263-010-0336-8

Download citation


  • Object detection
  • Color models
  • Learning
  • Photometric invariance
  • Combining classifiers
  • Diversified ensembles