Advertisement

Learning Artistic Lighting Template from Portrait Photographs

  • Xin Jin
  • Mingtian Zhao
  • Xiaowu Chen
  • Qinping Zhao
  • Song-Chun Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)

Abstract

This paper presents a method for learning artistic portrait lighting template from a dataset of artistic and daily portrait photographs. The learned template can be used for (1) classification of artistic and daily portrait photographs, and (2) numerical aesthetic quality assessment of these photographs in lighting usage. For learning the template, we adopt Haar-like local lighting contrast features, which are then extracted from pre-defined areas on frontal faces, and selected to form a log-linear model using a stepwise feature pursuit algorithm. Our learned template corresponds well to some typical studio styles of portrait photography. With the template, the classification and assessment tasks are achieved under probability ratio test formulations. On our dataset composed of 350 artistic and 500 daily photographs, we achieve a 89.5% classification accuracy in cross-validated tests, and the assessment model assigns reasonable numerical scores based on portraits’ aesthetic quality in lighting.

Keywords

Quantile Regression Equal Error Rate Local Contrast Aesthetic Quality Lighting Usage 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Hurter, B.: The best of photographic lighting — techniques and images for digital photographers, 2nd edn. Amherst Media (2007)Google Scholar
  3. 3.
    Tong, H., Li, M., Zhang, H., He, J., Zhang, C.: Classification of digital photos taken by photographers or home users. PCM (1), 198–205 (2004)Google Scholar
  4. 4.
    Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: CVPR, pp. 419–426 (2006)Google Scholar
  5. 5.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Studying aesthetics in photographic images using a computational approach. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, Part 3, vol. 3953, pp. 288–301. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Luo, Y., Tang, X.: Photo and video quality evaluation: Focusing on the subject. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 386–399. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Wong, L.K., Low, K.L.: Saliency-enhanced image aesthetics class prediction. In: ICIP (2009)Google Scholar
  8. 8.
    Li, C., Chen, T.: Aesthetic visual quality assessment of paintings. IEEE Journal of Selected Topics in Signal Processing 3, 236–252 (2009)CrossRefGoogle Scholar
  9. 9.
    Hunter, F., Biver, S., Fuqua, P.: Light: Science and Magic: An Introduction to Photographic Lighting, 3rd edn. Focal Press (2007)Google Scholar
  10. 10.
    Grey, C.: Master Lighting Guide for Portrait Photographers. Amherst Media (2004)Google Scholar
  11. 11.
    Prakel, D.: Basics Photography: Lighting. AVA Publishing (2007)Google Scholar
  12. 12.
    Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–714 (1986)CrossRefGoogle Scholar
  13. 13.
    Lin, J.: Divergence measures based on the shannon entropy. IEEE Trans. Info. Theory 37 (1991)Google Scholar
  14. 14.
    Della Pietra, S., Della Pietra, V., Lafferty, J.: Inducing features of random fields. IEEE Trans. Pattern Anal. Mach. Intell. 19, 380–393 (1997)CrossRefGoogle Scholar
  15. 15.
    Si, Z., Gong, H., Wu, Y.N., Zhu, S.C.: Learning mixed templates for object recognition. In: CVPR, pp. 272–279 (2009)Google Scholar
  16. 16.
    Friedman, J.H.: Exploratory projection pursuit. Journal of American Stat. Assoc. 82, 249–266 (1987)zbMATHCrossRefGoogle Scholar
  17. 17.
    Schwarz, G.: Estimating the dimension of a model. Ann. Statist. 6 (1978)Google Scholar
  18. 18.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Faraway, J.J.: Extending the Linear Model with R. Taylor & Francis Group, Abington (2006)zbMATHGoogle Scholar
  20. 20.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, Part 2, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  21. 21.
    Koenker, R.: Quantile Regression. Cambridge University Press, Cambridge (2005)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Xin Jin
    • 1
  • Mingtian Zhao
    • 2
    • 3
  • Xiaowu Chen
    • 1
  • Qinping Zhao
    • 1
  • Song-Chun Zhu
    • 2
    • 3
  1. 1.State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.Lotus Hill InstituteEzhouChina
  3. 3.Department of StatisticsUniversity of CaliforniaLos AngelesUSA

Personalised recommendations