Deep Lambertian networks

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 2, Issue , 2012, Pages 1623-1630

  Tang, Yichuan ^a   Salakhutdinov, Ruslan ^a   Hinton, Geoffrey ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

2D IMAGES; GENERATIVE MODEL; ILLUMINATION INVARIANT; ILLUMINATION VARIATION; LAMBERTIAN; LAMBERTIAN REFLECTANCE; LATENT VARIABLE; SINGLE IMAGES; SURFACE NORMALS; VISUAL PERCEPTION;

EQUIVALENCE CLASSES; FACE RECOGNITION; LEARNING SYSTEMS; LIGHT SOURCES;

SOLAR RADIATION;

EID: 84867114782     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Barrow, H. G. and Tenenbaum, J. M. Recovering intrinsic scene characteristics from images. In CVS78, pp. 3-26, 1978.
2. Belhumeur, P. N. and Kriegman, D. J. What is the set of images of an object under all possible lighting conditions. In CVPR, pp. 270-277, 1996.
3. Blanz, V. and Vetter. T. A morphable model for the synthesis of 3-D faces. In SIGGraph-99, pp. 187-194, 1099.
4. Duane, S., Kennedy, A. D., Pendleton, B. J, and Roweth, D. Hybrid Monte Carlo. Physics Letters B, 195(12):216-222, 1987.
5. Gehler, P., Rother, C., Kiefel, M., Zhang, L., and Schölkopf, B. Recovering intrinsic images with a global sparsity prior on reflectance. In NIPS, 2011.
6. Georghiades, Athinodoros S., Belhumeur, Peter N., and Kriegman, David J. From few to many: Illumination cone models for face recognition under variable lichting and pose. IEEE Trans. Pattern Anal Mach. Intell. 23 (6):643-606, 2001.
7. Geusebroek, J. M., Burghouts, G. J., and Smeulders, A. W. M. The amsterdam library of object images. International Journal of Computer Visum, 61(1), January 2005.
8. Hayakawa, H. Photometric stereo under a light-source with arbitrary motion. Journal of the Optical Society of America, 11(11):3079-3089, November 1994.
9. Hinton, G. K. Training products of experts by minimizing contrastive divergence. Neural Computation, 14:1771-1800, 2002.
10. Hinton, G. E. and Salakhutdinov, R. Reducing the dimensionality of data with neural networks. Science, 313: 504-507, 2006.
11. Hinton, G. E., Osindero, S., and Teh, Y. W. A fast learning algorithm for deep belief nets. Neural Computation, 18 (7):1527-1554, 2006.
12. Kleffner, Dorothy A. and Ramachandran, V. S. On the perception of shape from shading. Perception and Psychophysics, 52:18-36, 1992.
13. Krizhevsky, A. Learning multiple layers of features from tiny images, 2009. URL http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf.
14. Lee, H., Grosse, R., Ranganath, R., and Ng, A. Y. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Intl. Conf. on Machine Learning, pp. 609-616, 2009.
15. Lee, K. C., Ho, Jeffrey, and Kriegman, David. Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27:684-698, 2005.
16. Mohamed, A., Dahl, G., and Hinton, G. Acoustic modeling using deep belief networks. IEEE Transactions on Audio, Speech, and Language. Processing, 2011.
17. Neal, R. M. MCMC using Hamiltonian dynamics, in Handbook of Markov Chain. Monte Carlo (eds S. Brooks, A. Gelman, G. Jones, XL Meng). Chapman and Hall/CRC Press, 2010.
18. Neal, R. M. and Hinton, G. E. A new view of the EM algorithm that justifies incremental, sparse and other variants. In Jordan, M. I. (ed.), Learning in Graphical Models, pp. 355-368, 1998.
19. Quoc, L., Ngiam, J., Chen, Z., Chia, D., Koh, P. W., and Ng, A. Tiled convolutional neural networks. In NIPS 23, 2010.
20. Ranzato, M. and Hinton, G. Modeling pixel means and covariances using factorized third-order boltzmann machines. CVPR, 2010.
21. Susskind, J.M., Anderson, A.K., and Hinton, G.E. The Toronto Face Database. Technical report, 2011. http://aclab.ca/users/josh/TFD.html.
22. Taylor, Graham W., Fergus, Rob, LeCun, Yann, and Bregler, Christoph. Convolutional learning of spatio-temporal features. In ECCV 2010. Springer, 2010. ISBN 978-3-642-15566-6. URL http://dx.doi.org/10.1007/978-3-642-15567-3.
23. Wang, Y., Zhang, L., Liu, Z. C., Hua, G. Wen, Z., Zhang, Z. Y., and Samaras, D. Face relighting from a single image under arbitrary unknown lighting conditions. IEEE Trans. Pattern Analysis and Machine Intelligence, 31 (11):1968-1984, November 2009.
24. Woodham, R. J. Photometric method for determining surface orientation from multiple images. Optical Engineering, 19(1):139-144, January 1980.
25. Yuille, A. L., Snow, D., Epstein, R., and Belhumeur, P. N. Determining generative models of objects under varying illumination: Shape and albedo from multiple images using SVD and integrability. International Journal of Computer Vision, 35(3):203-222, December 1999.
26. Zhang, L. and Samaras, D. Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics. IEEE Trans. Pattern Analysis and Machine Intelligence, 28(3):351-363, March 2006.