The joint manifold model for semi-supervised multi-valued regression

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2007, Pages

  Navaratnam, Ramanan ^a   Fitzgibbon, Andrew W ^b   Cipolla, Roberto ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER NETWORKS; COMPUTER VISION; CONFORMAL MAPPING; IMAGE PROCESSING;

SEMI-SUPERVISED;

MATHEMATICAL MODELS;

EID: 50649114839     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2007.4408976     Document Type: Conference Paper

References (22)

1. A. Agarwal and B. Triggs. 3D human pose from silhouettes by relevant vector regression. In CVPR, 2004.
2. A. Agarwal and B. Triggs. Monocular human motion capture with a mixture of regressors. In IEEE Workshop on Vision for HCI at CVPR, 2005.
3. C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
4. Z. Ghahramani and M. I. Jordan. Supervised learning from incomplete data via an EM approach. In Proc. NIPS, volume 6, pages 120-127, 1994.
5. K. Grochow, S. L. Martin, A. Hertzmann, and Z. Popovi. Style-based inverse kinematics. ACM Transactions on Graphics, 23(3):522-531, 2004.
6. J. Ham, D. Lee, and L. Saul. Semisupervised alignment of manifolds. In AISTATS, pages 120-127, 2005.
7. J. H. Ham, I. Ahn, and D. Lee. Learning a manifold-constrained map between image sets: applications to matching and pose estimation. In CVPR, 2006.
8. M. Hu. Visual pattern recognition by moment invariants. IRE Trans. Information Theory, (2), 1962.
9. N. Lawrence. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. In J. Machine Learning Res., pages 1783-1816, 2005.
10. N. Lawrence and M. I. Jordan. Semi-supervised learning via Gaussian processes. In Proc. NIPS, 2005.
11. A. F. Moller. A scaled conjugate gradient algorithm for fast supervised learning. In Neural Networks, volume 6, pages 525-533, 1993.
12. R. Navaratnam, A. W. Fitzgibbon, and R. Cipolla. Semi-supervised learning of joint density models for human pose estimation. In Proc. BMVC, pages 679-688, Sept. 2006.
13. R. Navaratnam, A. W. Fitzgibbon, and R. Cipolla. The Joint Manifold Model, 2007. Website and technical report, http://www.research.microsoft. com/~awf/jmm.
14. A. P. Shon, K. Grochow, A. Hertzmann, and R. Rao. Learning shared latent structure for image synthesis and robotic imitation. In Proc. NIPS, pages 1233-1240, 2006.
15. C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas. Discriminative density propagation for 3D human motion estimation. In CVPR, 2005.
16. C. Sminchisescu, A. Kanaujia, Z. Li, and D. N. Metaxas. Conditional visual tracking in kernel space. In Proc. NIPS, 2005.
17. J. B. Tenenbaum, V. De Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. In Science, volume 290, pages 2319-2323, 2000.
18. T. P. Tian, R. Li, and S. Sclaroff. Articulated pose estimation in a learned smooth space of feasible solutions. In Proceedings of IEEE Workshop on Learning in Computer Vision and Pattern Recognition, 2005.
19. R. Urtasun, D. Fleet, and P. Fua. 3D people tracking with Gaussian process dynamical models. In CVPR, June 2006.
20. J. J. Verbeek. Learning nonlinear image manifolds by global alignment of local linear models. IEEE Trans. Pattern Analysis and Machine Intell., 28(8):1236-1250, 2006.
21. X. Zhu. Semi-supervised learning literature survey. CS TR 1530, Univ of Wisconsin-Madison, 2005.
22. http://mocap.cs.cmu.edu/.