AdaBoost on low-rank PSD matrices for metric learning

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

Volumn , Issue , 2011, Pages 2617-2624

  Bi, Jinbo ^a   Wu, Dijia ^b   Lu, Le ^b   Liu, Meizhu ^c   Tao, Yimo ^d   Wolf, Matthias ^b  

^a UNIVERSITY OF CONNECTICUT   (United States)

^b SIEMENS CORPORATE RESEARCH   (United States)

^c UNIVERSITY OF FLORIDA   (United States)

^d MICROSOFT   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTENT BASED RETRIEVAL; MEDICAL PROBLEMS; PATTERN RECOGNITION;

BENCHMARK DATASETS; BOOSTING ALGORITHM; COMPUTATION COSTS; CONTENT BASED IMAGE RETRIEVAL; DIFFUSE LUNG DISEASE; PAIRWISE CONSTRAINTS; POSITIVE SEMIDEFINITE MATRICES; SIMILARITY METRICS;

ADAPTIVE BOOSTING;

EID: 80052870741     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995363     Document Type: Conference Paper

References (21)

1. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall. Learning a Mahalanobis metric from equivalence constraints. JMLR, 6:937-965, 2005. 2617, 2623
2. A. Bar-Hillel and D. Weinshall. Learning distance functions by coding similarity. In ICML, 2007. 2617
3. J. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon. Information-theoretic metric learning. In ICML, pages 209-216, 2007. 2617, 2621, 2623
4. M. J. E. P. Xing, A. Y. Ng and S. Russell. Distance metric learning, with applicationto clustering with side-information. In NIPS, volume 15, pages 505-512, 2003. 2617, 2621, 2623
5. Y. Freund, R. Iyer, R. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4:933-969, 2003. 2620
6. Y. Freund and R. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. J. of Computer and System Sciences, 55(1):119-139, 1997. 2619
7. G. Fung, M. Dundar, B. Krishnapuram, and B. Rao. Multiple instance learning for computer aided diagnosis. In NIPS, pages 425-432, 2006. 2623
8. T. Hastie and R. Tibshirani. Discriminant adaptive nearest neighbor classification. IEEE Transaction on Pattern Analysis and Machine Intelligence, 18(6):607-616, 1996. 2617
9. X. He, O. King, W. Ma, M. Li, and H.-J. Zhang. Learning a semantic space from users relevance feedback for image retrieval. IEEE Trans. on CSVT, 13(1):39-48, 2003. 2617
10. Z. Lai and et al. Intra-patient supine-prone colon registration in ct colonography using shape spectrum. In MICCAI, pages 332-339, 2010. 2623
11. H. Müller, T. Pun, and D. Squire. Learning from user behavior in image retrieval: application of market basket analysis. International Journal of Computer Vision, 56(1-2):65-77, 2004. 2617
12. H. Peng, F. Long, and C. Ding. Feature selection based on mutual information: Criteria of max-dependency, maxrelevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 27(8):1226-1238, 2005. 2623
13. R. Rosales and G. Fung. Learning sparse metrics via linear programming. In Proceedings of ACM International Conference on Knowledge Discovery and Data Mining, 2006. 2617
14. H. Roth and et al. Establishing spatial correspondence between the inner colon surfaces from prone and supine ct colonography. In MICCAI, pages 497-504, 2010. 2623
15. R. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297-336, 1999. 2619
16. C. Shen, J. Kim, L. Wang, and A. van den Hengel. Positive semidefinite metric learning with boosting. Advances in Neural Information Processing Systems, 22, 2009. 2617, 2618, 2621, 2623
17. C. Shen, A. Welsh, and L. Wang. PSDBoost: matrix generation linear programming for positive semidefinite matrices learning. Advances in Neural Information Processing Systems, 21, 2008. 2617, 2618, 2621, 2623
18. S. Xiang, F. Nie, and C. Zhang. Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition, 41(12):3600-3612, 2008. 2617
19. J. Yu, J. Amores, N. Sebe, P. Radeva, and Q. Tian. Distance learning for similarity estimation. IEEE Trans. Pattern Anal. Mach. Intell., 30(3):451-462, 2008. 2620
20. S. Zhou, J. Shao, B. Georgescu, and D. Comaniciu. Boostmotion: boosting a discriminative similarity function for motion estimation. In IEEE Conf. on CVPR, 2006. 2617
21. H. Zhu, Y. Fan, H. Lu, and Z. Liang. Improving initial polyp candidate extraction for ct colonography. Phys Med Biol., 55(7):2087C2102, 2010. 2623