Robust multi-view boosting with priors

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

Volumn 6313 LNCS, Issue PART 3, 2010, Pages 776-789

  Saffari, Amir ^a   Leistner, Christian ^a   Godec, Martin ^a   Bischof, Horst ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BOOSTING; COMPUTER VISION; ITERATIVE METHODS; OBJECT RECOGNITION;

BOOSTING ALGORITHM; BOOSTING APPROACH; CLASSIFICATION MARGINS; COMPUTER VISION PROBLEMS; MULTI-VIEW LEARNING; OBJECT CATEGORY RECOGNITION; REGULARIZATION TERMS; VISUAL OBJECT TRACKING;

MACHINE LEARNING;

EID: 78149302163     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15558-1_56     Document Type: Conference Paper

References (30)

1. Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning, Cambridge, MA (2006)
2. Zhu, X.: Semi-supervised learning literature survey. Technical report (2008)
3. Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: COLT, pp. 92-100 (1998)
4. Brefeld, U., Büscher, C., Scheffer, T.: Multi-view discriminative sequential learning. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 60-71. Springer, Heidelberg (2005)
5. Sindhwani, V., Rosenberg, D.S.: An rkhs for multi-view learning and manifold co-regularization. In: ICML, pp. 976-983 (2008)
6. Leskes, B., Torenvliet, L.: The value of agreement a new boosting algorithm. J. Comput. Syst. Sci. 74, 557-586 (2008)
7. Levin, A., Viola, P., Freund, Y.: Unsupervised improvement of visual detectors using co-training. In: ICCV, vol. I, pp. 626-633 (2003)
8. Christoudias, C.M., Urtasun, R., Darrell, T.: Unsupervised distributed feature selection for multi-view object recognition. In: CVPR (2008)
9. Liu, R., Cheng, J., Lu, H.: A robust boosting tracker with minimum error bound in a co-training framework. In: ICCV (2009)
10. Tang, F., Brennan, S., Zhao, Q., Tao, H.: Co-tracking using semi-supervised support vector machines. In: ICCV (2007)
11. Sun, S., Zhang, Q.: Multiple-view multiple-learner semi-supervised learning. Technical report (2007)
12. Leistner, C., Saffari, A., Santner, J., Bischof, H.: Semi-supervised random forests. In: IEEE International Conference on Computer Vision, ICCV (2009)
13. Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: ICML, pp. 148-156 (1996)
14. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. The Annals of Statistics 38, 337-374 (2000)
15. Shirazi, H.M., Vasconcelos, N.: On the design of loss functions for classification: theory, robustness to outliers, and savageboost. In: NIPS, pp. 1049-1056 (2008)
16. Saffari, A., Grabner, H., Bischof, H.: SERBoost: Semi-supervised boosting with expectation regularization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 588-601. Springer, Heidelberg (2008)
17. Saffari, A., Leistner, C., Bischof, H.: Regularized multi-class semi-supervised boosting. In: CVPR (2009)
18. Friedman, J.: Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29, 1189-1232 (2001)
19. Breiman, L.: Random forests. Machine Learning 45, 5-32 (2001)
20. Zhu, J., Rosset, S., Zou, H., Hastie, T.: Multi-class adaboost. Technical report (2006)
21. Guruswami, V., Sahai, A.: Multiclass learning, boosting, and error-correcting codes. In: COLT (1999)
22. Zhang, B., Ye, G., Wang, Y., Xu, J., Herman, G.: Finding shareable informative patterns and optimal coding matrix for multiclass boosting. In: ICCV (2009)
23. Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.: Large scale multiple kernel learning. JMLR 7, 1531-1565 (2006) (Pubitemid 44373694)
24. Gehler, P., Nowozin, S.: On feature combination for multiclass object classification. In: ICCV (2009)
25. Avidan, S.: Ensemble tracking, vol. 2, pp. 494-501 (2005)
26. Grabner, H., Bischof, H.: On-line boosting and vision, vol. 1, pp. 260-267 (2006)
27. Grabner, H., Leistner, C., Bischof, H.: On-line semi-supervised boosting for robust tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234-247. Springer, Heidelberg (2008)
28. Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)
29. Ross, D., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. IJCV (2008)
30. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR (2006)