OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Editor: Stephen A. Burns
  • Vol. 25, Iss. 9 — Sep. 1, 2008
  • pp: 2286–2296

Spectral reflectance estimation from camera responses by support vector regression and a composite model

Wei-Feng Zhang and Dao-Qing Dai  »View Author Affiliations

JOSA A, Vol. 25, Issue 9, pp. 2286-2296 (2008)

View Full Text Article

Enhanced HTML    Acrobat PDF (545 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Regression methods are widely used to estimate the spectral reflectance of object surfaces from camera responses. These methods are under the same problem setting as that to build an estimation function for each sampled wavelength separately, which means that the accuracy of the spectral estimation will be reduced when the training set is small. To improve the spectral estimation accuracy, we propose a novel estimating approach based on the support vector regression method. The proposed approach utilizes a composite modeling scheme, which formulates the RGB values and the sampled wavelength together as the input term to make the most use of the information from the training samples. Experimental results show that the proposed method can improve the recovery accuracy when the training set is small.

© 2008 Optical Society of America

OCIS Codes
(300.6550) Spectroscopy : Spectroscopy, visible
(330.1710) Vision, color, and visual optics : Color, measurement

ToC Category:

Original Manuscript: March 6, 2008
Revised Manuscript: June 1, 2008
Manuscript Accepted: June 30, 2008
Published: August 18, 2008

Virtual Issues
Vol. 3, Iss. 11 Virtual Journal for Biomedical Optics

Wei-Feng Zhang and Dao-Qing Dai, "Spectral reflectance estimation from camera responses by support vector regression and a composite model," J. Opt. Soc. Am. A 25, 2286-2296 (2008)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. M. D. Fairchild, Color Appearance Models (Addison-Wesley, 1998).
  2. G. Hong, M. R. Luo, and P. A. Rhodes, “A study of digital camera colorimetric characterization based on polynomial modeling,” Color Res. Appl. 26, 76-84 (2001). [CrossRef]
  3. J. Y. Hardeberg, Acquisition and Reproduction of Color Images--Colorimetric and Multispectral Approaches (Dissertation.com, 2001).
  4. T. L. V. Cheung, S. Westland, D. R. Connah, and C. Ripamonti, “A comparative study of the characterization of color cameras by means of neural networks and polynomial transforms,” J. Coloration Technol. 120, 19-25 (2004). [CrossRef]
  5. T. Jetsu, V. Heikkinen, J. Parkkinen, M. Hauta-Kasari, B. Martinkauppi, S. D. Lee, H. W. Ok, and C. Y. Kim, “Color calibration of digital camera using polynomial transformation,” in CGIV, Third European Conference on Color in Graphics, Imaging and Vision (IS&T, 2006), pp. 163-166.
  6. H. Haneishi, T. Hasegawa, A. Hosoi, Y. Yokoyama, N. Tsumura, and Y. Miyake, “System design for accurately estimating the reflectance spectra of art paintings,” Appl. Opt. 39, 6621-6632 (2000). [CrossRef]
  7. N. Shimano, K. Terai, and M. Hironaga, “Recovery of spectral reflectances of objects being imaged by multispectral cameras,” J. Opt. Soc. Am. A 24, 3211-3219 (2007). [CrossRef]
  8. H. L. Shen, P. Q. Cai, S. J. Shao, and J. H. Xin, “Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation,” Opt. Express 15, 15545-15554 (2007). [CrossRef] [PubMed]
  9. P. Stigell, K. Miyata, and M. Hauta-Kasari, “Wiener estimation method in estimation of spectral reflectance from RGB images,” Pattern Recogn. Image Anal. 15, 327-329 (2005).
  10. N. Shimano, “Recovery of spectral reflectances of objects being imaged without prior knowledge,” IEEE Trans. Image Process. 15, 1848-1856 (2006). [CrossRef] [PubMed]
  11. J. Cohen, “Dependency of spectral reflectance curves of the Munsell color chips,” Appl. Opt. 39, 6621-6632 (1964). [CrossRef]
  12. L. T. Maloney, “Evaluation of linear model of surface spectral reflectance with small numbers of parameters,” J. Opt. Soc. Am. A 3, 1673-1683 (1986). [CrossRef] [PubMed]
  13. L. T. Maloney and B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,” J. Opt. Soc. Am. A 3, 29-33 (1986). [CrossRef] [PubMed]
  14. R. Schettini and S. Zuffi, “A computational strategy exploiting genetic algorithms to recover color surface reflectance functions,” Neural Comput. Appl. 16, 69-79 (2007).
  15. J. Y. Hardeberg, “Filter selection for multispectral color image acquistion,” J. Imaging Sci. Technol. 48, 105-110 (2004).
  16. D. Connah and J. Y. Hardeberg, “Spectral recovery using polynomial models,” Proc. SPIE 5667, pp. 65-75 (2005). [CrossRef]
  17. V. Heikkinen, T. Jetsu, J. Parkkinen, M. Hauta-Kasari, T. Jaaskelainen, and S. D. Lee, “Regularized learning framework in the estimation of reflectance spectra from camera responses,” J. Opt. Soc. Am. A 24, 2673-2683 (2007). [CrossRef]
  18. A. Mansouri, T. Silwa, J. Y. Hardeberg, and Y. Voisin, “Spectral reflectance reconstruction using wavelet basis decomposition,” in Proceedings of the 9th International Symposium on Multispectral Color Science and Application (MCS 07) (2007), pp. 149-155.
  19. L. J. Cao and F. E. H. Tay, “Support vector machine with adaptive parameters in financial time series forecasting,” IEEE Trans. Neural Netw. 14, 1506-1518 (2003). [CrossRef]
  20. X. Wang, A. Li, Z. H. Jiang, and H. Q. Feng, “Missing value estimation for DNA microarray gene expression data by support vector regression imputation and orthogonal coding scheme,” BMC Bioinf. 7, 32 (2006). [CrossRef]
  21. H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process. 16, 349-366 (2007). [CrossRef] [PubMed]
  22. V. N. Vapnik, The Nature of Statistical Learning Theory (Springer-Verlag, 1995).
  23. V. N. Vapnik, Statistical Learning Theory (Wiley, 1998).
  24. T. Evgeniou, M. Pontil, and T. Poggio, “Regularization networks and support vector machines,” Adv. Comput. Math. 13, 1-50 (2000). [CrossRef]
  25. A. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput. 14, 199-222 (2004). [CrossRef]
  26. M. Bertero, T. Poggio, and V. Torre, “Ill-posed problems in early vision,” Proc. IEEE 76, 869-889 (1988). [CrossRef]
  27. A. N. Tikhonov and V. Y. Arsenin, Solutions of Ill-Posed Problems (W. H. Winston, 1977).
  28. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis (Cambridge U. Press, 2004). [CrossRef]
  29. A. Rakotomamonjy and S. Canu, “Frames, reproducing kernels, regularization and learning,” J. Mach. Learn. Res. 6, 1485-1515 (2005).
  30. W. F. Zhang, D. Q. Dai, and H. Yan, “On a new class of framelet kernels for support vector regression and regularization networks,” in Advances in Knowledge Discovery and Data Mining (Springer, 2007), Vol. 4426, pp. 355-366. [CrossRef]
  31. T. Joachims, “Making large-scale SVM learning practical,” in Advances in Kernel Methods-Support Vector Learning (MIT Press, 1999), pp. 169-184.
  32. J. C. Platt, “Fast training of support vector machines using sequential minimal optimization,” in Advances in Kernel Methods-Support Vector Learning (MIT Press, 1999), pp. 185-208.
  33. R. E. Fan, P. H. Chen, and C. J. Lin, “Working set selection using second order information for training SVM,” J. Mach. Learn. Res. 6, 1889-1918 (2005).
  34. C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” 2001. Software available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm.
  35. Spectral Database, University of Joensuu Color Group, http://spectral.joensuu.fi/.
  36. Available athttp://www.cs.sfu.ca/colour/data/.
  37. V. Cheung, S. Westland, C. Li, J. Hardeberg, and D. Connah, “Characterization of trichromatic color cameras by using a new multispectral imaging technique,” J. Opt. Soc. Am. A 22, 1231-1240 (2005). [CrossRef]
  38. H. L. Shen and J. H. Xin, “Estimation of spectral reflectance of object surfaces with the consideration of perceptual color space,” Opt. Lett. 32, 96-98 (2007). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


Fig. 1 Fig. 2 Fig. 3
Fig. 4 Fig. 5

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited