Boosting invariance and efficiency in supervised learning

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2007, Pages

  Vedaldi, Andrea ^a   Favaro, Paolo ^b   Grisan, Enrico ^c  

^a University of California   (United States)

^b HERIOT WATT UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF PADOVA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARSENIC; ARTIFICIAL INTELLIGENCE; BOOLEAN FUNCTIONS; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COMPUTER NETWORKS; COMPUTER VISION; GRADIENT METHODS; IMAGE PROCESSING; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; OCCUPATIONAL RISKS; OPTIMIZATION; SUPERVISED LEARNING;

INTERNATIONAL CONFERENCES; RISK MINIMIZATION; TANGENT DISTANCE;

CONTROL THEORY;

EID: 50649111138     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2007.4408840     Document Type: Conference Paper

References (20)

1. http://www.cs.ubc.ca/~pcarbo/viola-traindata.tar.gz. Viola-Jones face database.
2. O. Chapelle, J. Weston, L. Bottou, and V. Vapnik. Vicinal risk minimization. In NIPS, pages 416-422, 2000.
3. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern classification. John Wiley and Sons, Inc., 2nd edition, 2001.
4. J. H. Friedman. Greedy function approximation: a gradient boosting machine. The Annals of Statistics, 2001.
5. J. H. Friedman, T. Hastie, and R. Tibshirani. Special invited paper. additive logistic regression: A statistical view of boosting. The Annals of Statistics, 28(2):337-374, 2000.
6. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell., 23(6):643-660, 2001.
7. F. Girosi and N. T. Chan. Prior knowledge and the creation of "virtual" examples for rbf networks. In Neural Networks for Signal Processing, pages 201-21, 1995.
8. T. Hastie, R. Tibishirani, and J. Friedman. The Elements of Statistical Learning. Springer, 2001.
9. D. G. Lowe. Object recognition from local scale-invariant features. In International Conference on Computer Vision, volume 2, pages 1150-1157, 1999.
10. H. Murase and S. K. Nayar. Visual learning and recognition of 3-d objects from appearance. Int. J. Comput. Vision, 14(1):5-24, 1995.
11. D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In Computer Vision and Pattern Recognition, pages 2161-2168, 2006.
12. C. P. Papageorgiou, M. Oren, and T. Poggio. A general framework for object detection. In International Conference on Computer Vision, pages 555-562, 1998.
13. A. Pozdnoukhov and S. Bengio. Invariances in kernel methods: From samples to objects. Pattern Recogn. Lett., 27(10):1087-1097, 2006.
14. R. E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. In Computational learning theory, pages 80-91, 1998.
15. B. Scholkopf, C. Burges, and V. Vapnik. Incorporating invariances in support vector learning machines. In Artificial Neural Networks, pages 47-52, 1996.
16. P. Simard, B. Victorri, Y. LeCun, and J. Denker. Tangent prop-a formalism for specifying selected invariances in an adaptive network. Advances in Neural Information Processing Systems 4, pages 895-903, 1992.
17. J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. In International Conference on Computer Vision, page 1470, Washington, DC, USA, 2003. IEEE Computer Society.
18. M. Turk and A. Pentland. Eigenfaces for recognition. J. of Cognitive Neurosci., 3:71-86, 1991.
19. V. Vapnik. The nature of statistical learning theory. Springer-Verlag, Inc., New York, NY, USA, 1995.
20. P. Viola and M. J. Jones. Robust real-time face detection. International Journal of Computer Vision, 57(2):137-154, 2004.