P-N learning: Bootstrapping binary classifiers by structural constraints

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 49-56

  Kalal, Zdenek ^a   Matas, Jiri ^b   Mikolajczyk, Krystian ^a  

^a UNIVERSITY OF SURREY   (United Kingdom)

^b CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFIERS; ITERATIVE PROCESS; LEARNING PROCESS; OBJECT DETECTORS; ONLINE LEARNING; STRUCTURAL CONSTRAINTS; SYNTHETIC AND REAL DATA; TRAINING SETS; UNLABELED DATA; VIDEO SEQUENCES;

ANIMALS; CLASSIFIERS; COMPUTER VISION; VIDEO RECORDING;

CLASSIFICATION (OF INFORMATION);

EID: 77956005443     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5540231     Document Type: Conference Paper

References (24)

1. Y. Abu-Mostafa. Machines that learn from hints. Scientific American, 272(4):64-71, 1995. 2
2. S. Avidan. Ensemble tracking. PAMI, 29(2):261-271, 2007. 2, 7
3. B. Babenko, M.-H. Yang, and S. Belongie. Visual tracking with online multiple instance learning. CVPR, 2009. 3, 7
4. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. COLT, 1998. 1, 2
5. L. Breiman. Random forests. ML, 45(1):5-32, 2001. 5
6. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006. 1, 2
7. R. Collins, Y. Liu, and M. Leordeanu. Online selection of discriminative tracking features. PAMI, 27(10):1631-1643, 2005. 2, 7
8. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. CVPR, 2, 2003. 2
9. H. Grabner and H. Bischof. On-line boosting and vision. CVPR, 2006. 2
10. O. Javed, S. Ali, and M. Shah. Online detection and classification of moving objects using progressively improving detectors. CVPR, 2005. 2
11. Z. Kalal, J. Matas, and K. Mikolajczyk. Weighted sampling for large-scale boosting. BMVC, 2008. 5
12. Z. Kalal, J. Matas, and K. Mikolajczyk. Online learning of robust object detectors during unstable tracking. OLCV, 2009. 3, 5, 7
13. V. Lepetit, P. Lagger, and P. Fua. Randomized trees for real-time keypoint recognition. CVPR, 2005. 5
14. A. Levin, P. Viola, and Y. Freund. Unsupervised improvement of visual detectors using co-training. ICCV, 2003. 2
15. J. Lim, D. Ross, R. Lin, and M. Yang. Incremental learning for visual tracking. NIPS, 2005. 2, 7
16. B. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. IJCAI, 81:674-679, 1981. 5
17. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Machine learning, 39(2):103-134, 2000. 1, 2
18. M. Ozuysal, P. Fua, and V. Lepetit. Fast keypoint recognition in ten lines of code. CVPR, 2007. 5
19. N. Poh, R. Wong, J. Kittler, and F. Roli. Challenges and research directions for adaptive biometric recognition systems. ICAB, 2009. 2
20. C. Rosenberg, M. Hebert, and H. Schneiderman. Semi-supervised self-training of object detection models. WACV, 2005. 2
21. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. CVPR, 2001. 4
22. Q. Yu, T. Dinh, and G. Medioni. Online tracking and reacquisition using co-trained generative and discriminative trackers. ECCV, 2008. 3, 6, 7
23. K. Zhou, J. Doyle, and K. Glover. Robust and optimal control. Prentice Hall Englewood Cliffs, NJ, 1996. 4
24. X. Zhu and A. Goldberg. Introduction to semi-supervised learning. Morgan & Claypool Publishers, 2009. 1, 2