On-line semi-supervised multiple-instance boosting

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

Volumn , Issue , 2010, Pages 1879-1886

  Zeisl, Bernhard ^a,b   Leistner, Christian ^a   Saffari, Amir ^a   Bischof, Horst ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

COHERENT FRAMEWORKS; MULTIPLE-INSTANCE LEARNING; ON-LINE CLASSIFIER; ROBUST LEARNING; SELF-LEARNING; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; SEMI-SUPERVISED METHOD; STATE-OF-THE-ART METHODS; UNLABELED SAMPLES;

COMPUTER VISION; LEARNING ALGORITHMS; SUPERVISED LEARNING;

COMPUTATIONAL METHODS;

EID: 77955991184     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5539860     Document Type: Conference Paper

References (21)

1. S. Avidan. Ensemble tracking. PAMI, 29(2):261-271, 2007.
2. B. Babenko, M.-H. Yang, and S. Belongie. Visual Tracking with Online Multiple Instance Learning. In CVPR, 2009.
3. S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. PAMI, 24(4):509-522, 2002.
4. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. Cambridge, MA, 2006.
5. D. Comaniciu, V. Ramesh, and P. Meer. Real-time tracking of non-rigid objects using mean shift. In CVPR, volume 2, pages 142-149, 2000.
6. J. Friedman. Greedy function approximation: A gradient boosting machine. The annals of statistics, pages 1189-1232, 2001.
7. A. Goldberg, M. Li, and X. Zhu. Online manifold regularization: A new learning setting and empirical study. In ECCV, 2008.
8. H. Grabner and H. Bischof. On-line boosting and vision. In CVPR, volume 1, pages 260-267, 2006.
9. H. Grabner, M. Grabner, and H. Bischof. Real-time tracking via on-line boosting. In BMVC, 2006.
10. H. Grabner, C. Leistner, and H. Bischof. Semi-supervised on-line boosting for robust tracking. In ECCV, 2008.
11. C. Leistner, A. Saffari, P. Roth, and H. Bischof. On robustness of on-line boosting - a competitive study. In OLCV, 2009.
12. Y. Li, H. Ai, T. Yamashita, S. Lao, and M. Kawade. Tracking in low frame rate video: A cascade particle filter with discriminative observers of different lifespans. In CVPR, pages 1-8, 2007.
13. Z. Lin, G. Hua, and L. Davis. Multiple instance feature for robust part-based object detection. In CVPR, 2009.
14. I. Matthews, T. Ishikawa, and S. Baker. The template update problem. PAMI, pages 810-815, 2004.
15. N. C. Oza and S. Russell. Online bagging and boosting. In Proc. Artificial Intelligence and Statistics, pages 105-112, 2001.
16. M. Özuysal, P. Fua, and V. Lepetit. Fast keypoint recognition in ten lines of code. In CVPR, 2007.
17. A. Saffari, H. Grabner, and H. Bischof. Semi-supervised boosting via expectation regularization. In ECCV, 2008.
18. J. Shi and C. Tomasi. Good features to track. In CVPR, 1994.
19. S. Stalder, H. Grabner, L. van Gool, E. Zurich, and K. Leuven. Beyond Semi-Supervised Tracking: Tracking Should Be as Simple as Detection, but not Simpler than Recognition. In ICCV, WS on On-line Learning for Computer Vision, 2009.
20. P. Viola, J. Platt, and C. Zhang. Multiple instance boosting for object detection. In NIPS, volume 18, pages 1417-1424. MIT Press, 2006.
21. A. Yilmaz, O. Javed, and M. Shah. Object tracking: A survey. ACM Comput. Surv., 2006.