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.
Chapter PDF
Similar content being viewed by others
Keywords
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
Wikipedia, http://en.wikipedia.org/wiki/photography
Hurter, B.: The best of photographic lighting — techniques and images for digital photographers, 2nd edn. Amherst Media (2007)
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)
Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: CVPR, pp. 419–426 (2006)
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)
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)
Wong, L.K., Low, K.L.: Saliency-enhanced image aesthetics class prediction. In: ICIP (2009)
Li, C., Chen, T.: Aesthetic visual quality assessment of paintings. IEEE Journal of Selected Topics in Signal Processing 3, 236–252 (2009)
Hunter, F., Biver, S., Fuqua, P.: Light: Science and Magic: An Introduction to Photographic Lighting, 3rd edn. Focal Press (2007)
Grey, C.: Master Lighting Guide for Portrait Photographers. Amherst Media (2004)
Prakel, D.: Basics Photography: Lighting. AVA Publishing (2007)
Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–714 (1986)
Lin, J.: Divergence measures based on the shannon entropy. IEEE Trans. Info. Theory 37 (1991)
Della Pietra, S., Della Pietra, V., Lafferty, J.: Inducing features of random fields. IEEE Trans. Pattern Anal. Mach. Intell. 19, 380–393 (1997)
Si, Z., Gong, H., Wu, Y.N., Zhu, S.C.: Learning mixed templates for object recognition. In: CVPR, pp. 272–279 (2009)
Friedman, J.H.: Exploratory projection pursuit. Journal of American Stat. Assoc. 82, 249–266 (1987)
Schwarz, G.: Estimating the dimension of a model. Ann. Statist. 6 (1978)
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)
Faraway, J.J.: Extending the Linear Model with R. Taylor & Francis Group, Abington (2006)
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)
Koenker, R.: Quantile Regression. Cambridge University Press, Cambridge (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jin, X., Zhao, M., Chen, X., Zhao, Q., Zhu, SC. (2010). Learning Artistic Lighting Template from Portrait Photographs. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15561-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-15561-1_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15560-4
Online ISBN: 978-3-642-15561-1
eBook Packages: Computer ScienceComputer Science (R0)