Efficient approximations for support vector machines in object detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3175, Issue , 2004, Pages 54-61

  Kienzle, Wolf ^a,b   Bakir, Gökhan ^a,b   Franz, Matthias ^a,b   Schölkopf, Bernhard ^a,b  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b Dept Schölkopf ^*  (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

FACE RECOGNITION; OBJECT DETECTION; OBJECT RECOGNITION; OPTIMIZATION; SUPPORT VECTOR MACHINES; VECTORS;

APPROXIMATION SCHEME; DECISION FUNCTIONS; HANDWRITTEN DIGIT; STRUCTURAL CONSTRAINTS; SUPPORT VECTOR; SYNTHETIC VECTOR; UNCONSTRAINED OPTIMIZATION;

PATTERN RECOGNITION;

EID: 33947236525     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-28649-3_7     Document Type: Article

References (12)

1. G .H. Bakir, A. Gretton, M.O. Franz, and B. Schölkopf. Multivariate regression via stiefel manifold constraints. Proc. of the Pattern Recognition Symposium, DAGM, 2004.
2. C. J. C. Burges. Simplified support vector decision rules. In L. Saitta, editor, Proceedings of the 13th International Conference on Machine Learning, pages 71-77, San Mateo, CA, 1996. Morgan Kaufmann.
3. C. J. C. Burges and B. Schölkopf. Improving the accuracy and speed of support vector learning machines. In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 375-381, Cambridge, MA, 1997. MIT Press.
4. B. Heisele, T. Poggio, and M. Pontil. Face detection in still gray images. Technical Report 1687, MIT A.I. Lab, 2000.
5. E. Osuna, R. Freund, and F. Girosi. Training support vector machines: An application to face detection. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pages 130-136, 1997.
6. S. Romdhani, P. Torr, B. Schölkopf, and A. Blake. Fast face detection, using a sequential reduced support vector evaluation. In Proceedings of the International Conference on Computer Vision, 2001.
7. H. Rowley, S. Baluja, and T. Kanade. Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20:23-38, 1998.
8. H. Schneiderman. A statistical approach to 3d object detection applied to faces and cars. Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2000.
9. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
10. Ingo Steinwart. Sparseness of support vector machines - some asymptotically sharp bounds. In Sebastian Thrun, Lawrence Saul, and Bernhard Schölkopf, editors, Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2004.
11. K. Sung and T. Poggio. Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 1998.
12. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2001.