Augmenting crfs with boltzmann machine shape priors for image labeling

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2013, Pages 2019-2026

  Kae, Andrew ^a   Sohn, Kihyuk ^b   Lee, Honglak ^b   Learned Miller, Erik ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

attributes; deep learning; face processing; segmentation

Indexed keywords

ATTRIBUTES; BOLTZMANN MACHINES; CONDITIONAL RANDOM FIELDS(CRFS); DEEP LEARNING; FACE PROCESSING; LOCAL INTERACTIONS; RESTRICTED BOLTZMANN MACHINE; SEGMENTATION MODELS;

COMPLEX NETWORKS; IMAGE SEGMENTATION;

FACE RECOGNITION;

EID: 84887349828     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.263     Document Type: Conference Paper

References (32)

1. vis-www. cs. umass. edu/GLOC/.
2. vis-www. cs. umass. edu/lfw/part
3. P. Arbeláez, B. Hariharan, C. Gu, S. Gupta, L. Bourdev, and J. Malik. Semantic segmentation using regions and parts. In CVPR, 2012.
4. E. Borenstein, E. Sharon, and S. Ullman. Combining topdown and bottom-up segmentation. In CVPR Workshop on Perceptual Organization in Computer Vision, 2004.
5. S. M. A. Eslami, N. Heess, and J. Winn. The shape Boltzmann machine: A strong model of object shape. In CVPR, 2012.
6. S. M. A. Eslami and C. K. I. Williams. A generative model for parts-based object segmentation. In NIPS, 2012.
7. X. He, R. Zemel, and M. Carreira-Perpinán. Multiscale conditional random fields for image labeling. In CVPR, 2004.
8. X. He, R. Zemel, and D. Ray. Learning and incorporating top-down cues in image segmentation. In ECCV, 2006.
9. G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1771-1800, 2002.
10. G. B. Huang, V. Jain, and E. Learned-Miller. Unsupervised joint alignment of complex images. In ICCV, 2007.
11. G. B. Huang, H. Lee, and E. Learned-Miller. Learning hierarchical representations for face verification with convolutional deep belief networks. In CVPR, 2012.
12. G. B. Huang, M. Mattar, H. Lee, and E. Learned-Miller. Learning to align from scratch. In NIPS, 2012.
13. G. B. Huang, M. Narayana, and E. Learned-Miller. Towards unconstrained face recognition. In CVPR Workshop on Perceptual Organization in Computer Vision, 2008.
14. G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst, 2007.
15. N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar. Attribute and simile classifiers for face verification. In ICCV, 2009.
16. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001.
17. Q. V. Le, M. A. Ranzato, R. Monga, M. Devin, K. Chen, G. S. Corrado, J. Dean, and A. Y. Ng. Building high-level features using large scale unsupervised learning. In ICML, 2012.
18. Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F. Huang. A tutorial on energy-based learning. In G. Bakir, T. Hofman, B. Schölkopf, A. Smola, and B. Taskar, editors, Predicting Structured Data. MIT Press, 2006.
19. K. Lee, D. Anguelov, B. Sumengen, and S. Gokturk. Markov random field models for hair and face segmentation. In FG, 2008.
20. J. Malik, S. Belongie, J. Shi, and T. Leung. Textons, contours and regions: Cue integration in image segmentation. In ICCV, 1999.
21. D. Martin, C. Fowlkes, and J. Malik. Learning to detect natural image boundaries using brightness and texture. In NIPS, 2002.
22. V. Mnih, H. Larochelle, and G. Hinton. Conditional restricted boltzmann machines for structured output prediction. In UAI, 2011.
23. K. P. Murphy, Y. Weiss, and M. I. Jordan. Loopy belief propagation for approximate inference: An empirical study. In UAI, 1999.
24. M. Rastegari, A. Farhadi, and D. Forsyth. Attribute discovery via predictable discriminative binary codes. In ECCV, 2012.
25. R. Salakhutdinov and G. Hinton. Deep Boltzmann machines. In AISTATS, 2009.
26. L. Saul, T. Jaakkola, and M. Jordan. Mean field theory for sigmoid belief networks. Journal of Artificial Intelligence Research, 4:61-76, 1996.
27. C. Scheffler, J. Odobez, and R. Marconi. Joint adaptive colour modelling and skin, hair and clothing segmentation using coherent probabilistic index maps. In BMVC, 2011.
28. P. Smolensky. Information processing in dynamical systems: Foundations of harmony theory. Parallel distributed processing: Explorations in the microstructure of cognition, 1:194-281, 1986.
29. N. Wang, H. Ai, and S. Lao. A compositional exemplarbased model for hair segmentation. In ACCV, 2011.
30. N. Wang, H. Ai, and F. Tang. What are good parts for hair shape modeling? In CVPR, 2012.
31. L. Wolf, T. Hassner, and Y. Taigman. Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE-TPAMI, 33(10):1978-1990, 2011.
32. Y. Yacoob and L. Davis. Detection and analysis of hair. IEEE-PAMI, 28(7):1164-1169, 2006.