Boosting classifier cascades

Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Volumn , Issue , 2010, Pages

  Saberian, Mohammad J ^a   Vasconcelos, Nuno ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC DESIGN; BOOSTING ALGORITHM; BOOSTING CLASSIFIERS; CASCADE CONFIGURATION; CASCADE DESIGNS; CASCADE MODELING; FULLY AUTOMATED; LAGRANGIAN; OPTIMAL DESIGN; RECURSIVE COMPUTATION;

CLASSIFICATION (OF INFORMATION);

EID: 85162018114     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. S. C. Brubaker, M. D.Mullin, and J.M. Rehg. On the design of cascades of boosted ensembles for face detection. International Journal of Computer Vision, 77:65-86, 2008.
2. Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting, 1997.
3. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28:2000, 1998.
4. X. Hou, C.-L. Liu, and T. Tan. Learning boosted asymmetric classifiers for object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 330-338, 2006.
5. S. Z. Li and Z. Zhang. Floatboost learning and statistical face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26(9):1112-1123, 2004.
6. C. Liu and H.-Y. Shum;. Kullback-leibler boosting. In IEEE Conference on Computer Vision and Pattern Recognition, pages 587-594, 2003.
7. H. Luo. Optimization design of cascaded classifiers. In IEEE Conference on Computer Vision and Pattern Recognition, pages 480-485, 2005.
8. H. Masnadi-Shirazi and N. Vasconcelos. High detection-rate cascades for real-time object detection. In IEEE International Conference on Computer Vision, volume 2, pages 1-6, 2007.
9. J. Sochman and J. Matas. Waldboost - learning for time constrained sequential detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 150-157, 2005.
10. J. Sun, J. M. Rehg, and A. Bobick. Automatic cascade training with perturbation bias. IEEE Conference on Computer Vision and Pattern Recognition, 2:276-283, 2004.
11. K. K. Sung and T. Poggio. Example based learning for view-based human face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20:39-51, 1998.
12. P. Viola and M. Jones. Fast and robust classification using asymmetric adaboost and a detector cascade. In Advances in Neural Information Processing System, pages 1311-1318, 2001.
13. P. Viola and M. Jones. Robust real-time object detection. International Journal of Computer Vision, 57(2):137-154, 2004.
14. J. Wu, S. Brubaker, M. D. Mullin, and J. M. Rehg. Fast asymmetric learning for cascade face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 3:369-382, 2008.
15. R. Xiao, L. Zhu, and H.-J. Zhang. Boosting chain learning for object detection. In IEEE International Conference on Computer Vision, pages 709-715, 2003.