Image-based multiclass boosting and echocardiographic view classification

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

Volumn 2, Issue , 2006, Pages 1559-1565

  Kevin Zhou, S ^a   Park, J H ^a   Georgescu, B ^a   Simopoulos, C ^b   Otsuki, J ^b   Comaniciu, D ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b Ultrasound Division   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LOGITBOOSTING ALGORITHMS; MULTIPLE BINARY CLASSIFIERS; OBJECT DETECTION;

CLASSIFICATION (OF INFORMATION); IMAGE ANALYSIS; LEARNING SYSTEMS; MEDICAL IMAGING; OBJECT RECOGNITION; PROBLEM SOLVING;

ECHOCARDIOGRAPHY;

EID: 33845571599     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2006.146     Document Type: Conference Paper

References (17)

1. T. Dietterich and G. Bakiri. Solving multiclass learning problem via error-correcting output codes, Journal of Artificial Intelligence, 2:263-286, 1995.
2. S. Ebadollahi, S. Chang, and H. Wu. Automatic view recognition inechocardiogram videos using parts-based representation. In CVPR, pages 2-9, 2004.
3. Y. Freund and R. Schapire. A decision-theoretic generalization of online leaning and an application to boosting. Journal of Computer and System Sciences, 5(1):119.
4. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. Ann. Statist., 28(2):337-407, 2000.
5. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer, 2001.
6. S. Krempp, D. Geman, and Y. Amit. Sequential learning of reusable parts for object detection. Technical report, CS Johns Hopkins Univ., 2002.
7. S. Lazebnik, C. Schmid, and J. Ponce. Affine-invariant local descriptors and neighborhood statistics for texture recognition. In ICCV, 2003.
8. Y. LeCun, F. Huang, and L. Bottou. Learning methods for generic object recognition with invariance to pose and lighting. In CVPR, 2004.
9. F. Li, R. Fergus, and P. Perona. A bayesian approach to unsupervised one-shot learning of object categories, In ICCV, 2003.
10. S. Li and Z. Zhang. Floatboosting learning and statistical face detection. PAMI, 26(9), 2004.
11. D, Lowe. Object recognition from local scale-invariant features. In ICCV, 1999.
12. C. Papageorgiou, M. Oren, and T. Poggio. A general framework for object detection. In ICCV, 1998.
13. R. Schapire and Y. Singer. Improved boosting using confidence-rated predictions. Machine Learning, 37(3):297-336, 1999.
14. H. Tagare, F. Vos, C. Jaffe, and J. Duncan, Arrangement: A spatial relation between parts fo evaluating similarity of tomograhic section. PAMI, 17:880-893, 1995.
15. A. Torralba, K. Murphy, and W. Freeman. Sharing features: efficient boosting procedures for multiclass object detection. In CVPR, 2004.
16. Z. Tu. Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In ICCV, 2005.
17. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In CVPR, pages 511-518, 2001.