Shared kernel information embedding for discriminative inference

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Volumn , Issue , 2009, Pages 2852-2859

  Sigal, Leonid ^a   Memisevic, Roland ^a   Fleet, David J ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LARGE DATASET;

DISCRIMINATIVE METHODS; GLOBAL MODELING; IMAGE FEATURES; KERNEL INFORMATION EMBEDDING; LATENT VARIABLE MODELING; LATENT VARIABLE MODELS; MULTIPLE INPUTS; PARTIALLY LABELLED DATA;

LEARNING SYSTEMS;

EID: 70450181379     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206576     Document Type: Conference Paper

References (21)

1. A. Agarwal and B. Triggs. 3d human pose from silhouettes by relevance vector regression. CVPR, 2:882-888, 2004.
2. A. Agarwal and B. Triggs. Learning to track 3d human motion from silhouettes. ICML, 9-16, 2004.
3. L. Bo, C. Sminchisescu, A. Kanaujia, and D. Metaxas. Fast algorithms for large scale conditional 3d prediction. CVPR, 2008.
4. D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Trans. PAMI, 24(5):603-619, 2002.
5. T. de Campos and D. Murray. Regression-based hand pose estimation from multiple cameras. CVPR, 1:782-789, 2006.
6. C. Ek, P. Torr, and N. Lawrence. Gaussian process latent variable models for human pose estimation. Work. Mach. Learn. Multimodal Interact., LNCS 4892:132-143, 2007.
7. B. Han, D. Comaniciu, Y. Zhu, and L. Davis. Sequential kernel density approximation: Application to real-time visual tracking. IEEE Trans. PAMI, 30(7):1186-1197, 2008.
8. T. Jaeggli, E. Koller-Meier, and L. V. Gool. Monocular tracking with a mixture of view-dependent learned models. AMDO, LNCS 4069:494-503, 2006.
9. A. Kanaujia, C. Sminchisescu, and D. Metaxas. Semisupervised hierarchical models for 3d human pose reconstruction. CVPR, 2007.
10. A. Kanaujia, C. Sminchisescu, and D. Metaxas. Spectral latent variable models for perceptual inference. ICCV, 2007.
11. N. D. Lawrence. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J. Machine Learning Res., 6:1783-1816, Nov. 2005.
12. R.Memisevic. Kernel information embeddings. ICML, 633- 640, 2006.
13. R. Navaratnam, A. Fitzgibbon, and R. Cipolla. The joint manifold model for semi-supervised multi-valued regression. ICCV, 2007.
14. J. Quiñonero-Candela and C. Rasmussen. A unifying view of sparse approximate gaussian process regression. J. Machine Learning Res., 6:1939-1959, 2006.
15. V. Raykar and R. Duraiswami. The Improved Fast Gauss Transform with applications to machine learning. MIT Press, 2006.
16. G. Shakhnarovich, P. Viola, and T. Darrell. Fast pose estimation with parameter-sensitive hashing. ICCV, 2:750-759, 2003.
17. A. Shon, K. Grochow, A. Hertzmann, and R. Rao. Learning latent structure for image synthesis and robotic imitation. NIPS, 1233-1240, 2006.
18. L. Sigal, A. Balan, and M. J. Black. Combined discriminative and generative articulated pose and non-rigid shape estimation. NIPS, 2007.
19. L. Sigal and M. J. Black. Humaneva: Synchronized video and motion capture dataset for evaluation of articulated human motion. TR CS-06-08, Brown University, 2006.
20. C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas. Discriminative density propagation for 3d human motion estimation. CVPR, 1:390-397, 2005.
21. R. Urtasun and T. Darrell. Local probabilistic regression for activity-independent human pose inference. CVPR, 2008.