Learning to Traverse Image Manifolds

NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems

Volumn , Issue , 2006, Pages 361-368

  Dollár, Piotr ^a   Rabaud, Vincent ^a   Belongie, Serge ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE CODING;

DISTANCE ESTIMATION; EMBEDDINGS; FRAME RATE UP-CONVERSION; IMAGE MANIFOLDS; LEARN+; MANIFOLD LEARNING; OUT-OF-SAMPLE EXTENSION; SMOOTH MANIFOLDS; TANGENT DISTANCE; WARPING FUNCTION;

IMAGE COMPRESSION;

EID: 34547983537     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. E. Di Bernardo, L. Goncalves and P. Perona.US Patent 6,552,729: Automatic generation of animation of synthetic characters., 2003.
2. Y. Bengio and M. Monperrus. Non-local manifold tangent learning. In NIPS. 2005.
3. Y. Bengio, J.F. Paiement, P. Vincent, O. Delalleau, N. Le Roux, and M. Ouimet. Out-of-sample extensions for LLE, isomap, MDS, eigenmaps, and spectral clustering. In NIPS, 2004.
4. D. Beymer and T. Poggio. Face recognition from one example view. In ICCV, page 500, Washington, DC, USA, 1995. IEEE Computer Society.
5. Volker Blanz and Thomas Vetter. Face recognition based on fitting a 3D morphable model. PAMI, 25(9):1063-1074, 2003.
6. M. Brand. Charting a manifold. In NIPS, 2003.
7. Christoph Bregler, Michele Covell, and Malcolm Slaney. Video rewrite: driving visual speech with audio. In SIGGRAPH, pages 353-360, 1997.
8. E. Chuang, H. Deshpande, and C. Bregler. Facial expression space learning. In Pacific Graphics, 2002.
9. P. Dollár, V. Rabaud, and S. Belongie. Learning to traverse image manifolds. Technical Report CS2007-0876, UCSD CSE, Jan. 2007.
10. G. J. Edwards, T. F. Cootes, and C. J. Taylor. Face recognition using active appearance models. ECCV, 1998.
11. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer, 2001.
12. N. Jojic, B. Frey, and A. Kannan. Epitomic analysis of appearance and shape. In ICCV, 2003.
13. D. Keysers, W. Macherey, J. Dahmen, and H. Ney. Learning of variability for invariant statistical pattern recognition. ECML, 2001.
14. T. Moriyama, T. Kanade, J. Xiao, and J. F. Cohn. Meticulously detailed eye region model. PAMI, 2006.
15. H. Murase and S.K. Nayar. Visual learning and recognition of 3D objects from appearance. IJCV, 1995.
16. R. Rao and D. Ruderman. Learning Lie groups for invariant visual perception. In NIPS, 1999.
17. L. K. Saul and S. T. Roweis. Think globally, fit locally: unsupervised learning of low dimensional manifolds. JMLR, 2003.
18. A. Schödl, R. Szeliski, D.H. Salesin, and I. Essa. Video textures. In SIGGRAPH, 2000.
19. B. Schölkopf, A. Smola, and K. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neur. Comp., 1998.
20. P. Simard, Y. LeCun, and J. S. Denker. Efficient pattern recognition using a new transformation distance. In NIPS, 1993.
21. P. Simard, Y. LeCun, J. S. Denker, and B. Victorri. Transformation invariance in pattern recognition-tangent distance and tangent propagation. In Neural Networks: Tricks of the Trade, 1998.
22. J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2000.
23. Joshua B. Tenenbaum and William T. Freeman. Separating style and content with bilinear models. Neural Computation, 12(6):1247-1283, 2000.
24. K. Q. Weinberger and L. K. Saul. Unsupervised learning of image manifolds by semidefinite programming. In CVPR04.
25. Z. Zhang and Zha. Local linear smoothing for nonlinear manifold learning. Technical report, 2003.