Multi-instance learning with relational information of instances

2009 Workshop on Applications of Computer Vision, WACV 2009

Volumn , Issue , 2009, Pages

  Herman, Gunawan ^a   Ye, Getian ^a   Wang, Yang ^a   Xu, Jie ^a   Zhang, Bang ^a  

^a CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE SPACE; MULTI-INSTANCE LEARNING; TEXT CATEGORIZATION;

TECHNICAL PRESENTATIONS; TEXT PROCESSING;

COMPUTER VISION;

EID: 77951157561     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WACV.2009.5403078     Document Type: Conference Paper

References (23)

1. S. Andrews. Learning from Ambiguous Examples. PhD thesis, Brown University, 2007.
2. S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In NIPS, pages 561-568, 2003.
3. M. R. Boutell, J. Luo, X. Shen, and C. M. Brown. Learning multi-label scene classification. Pattern Recognition, 37(9):1757-1771, 2004.
4. C. Carson, M. Thomas, S. Belongie, J. M. Hellerstein, and J. Malik. Blobworld: A system for region-based image indexing and retrieval. In Intl. Conf. Visual Inf. Syst., pages 509-516, 1999.
5. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines, 2001.
6. Y. Chen, J. Bi, and J. Z. Wang. MILES: Multiple-instance learning via embedded instance selection. IEEE TPAMI, 28(12):1931-1947, 2006.
7. Y. Chen and J. Wang. Image categorization by learning and reasoning with regions. JMLR, 5:913-939, 2004.
8. N. Cristianini, J. Kandola, A. Elisseeff, and J. Shawe-Taylor. On kernel-target alignment. In NIPS, 2001.
9. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89:31-71, 1997.
10. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In CVPR, pages 264-271, 2003.
11. Z. Fu and A. Robles-Kelly. An instance selection approach to multiple instance learning. In CVPR, 2009.
12. T. Gärtner, P. Flach, A. Kowalczyk, and A. Smola. Multiinstance kernels. In ICML, 2002.
13. W.-J. Li and D.-Y. Yeung. Localized context-based image retrieval through evidence region identification. In CVPR, 2009.
14. H.-T. Lin, C.-J. Lin, and R. C. Weng. A note on Platt's probabilistic outputs for support vector machines. Machine Learning, 68:267-276, 2007.
15. O. Maron and A. L. Ratan. Multiple-instance learning for natural scene classification. In ICML, 1998.
16. R. E. Schapire and Y. Singer. BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2-3):135-168, 2000.
17. C. Schmid and R. Mohr. Local grayvalue invariants for image retrieval. IEEE TPAMI, 19(5):530-535, 1997.
18. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, Cambridge, MA, USA, 2002.
19. H.-Y. Wang, Q. Yang, and H. Zha. Adaptive p-posterior mixture model kernels for multiple instance learning. In ICML, pages 1136-1143, 2008.
20. Q. Zhang and S. A. Goldman. EM-DD: An improved multiple-instance learning technique. In NIPS, 2002.
21. Z.-H. Zhou, Y.-Y. Sun, and Y.-F. Li. Multi-instance learning by treating instances as non-i.i.d. samples. In ICML, 2009.
22. Z.-H. Zhou and J.-M. Xu. On the relation between multiple-instance learning and semi-supervised learning. In ICML, pages 1167-1174, 2007.
23. Z.-H. Zhou and M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In NIPS, pages 1609-1616, 2006.