Concurrent multiple instance learning for image categorization

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

Volumn , Issue , 2007, Pages

  Qi, Guo Jun ^a   Hua, Xian Sheng ^b   Rui, Yong ^b   Mei, Tao ^b   Tang, Jinhui ^a   Zhang, Hong Jiang ^b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FACTORIZATION; LEARNING SYSTEMS; PROBLEM SOLVING; SEMANTICS; TENSORS;

CATEGORIZATION PERFORMANCES; KERNEL HUBERT SPACE; MULTIPLE INSTANCE LEARNING;

IMAGE RECOGNITION;

EID: 35148866185     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2007.383152     Document Type: Conference Paper

References (20)

1. S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In Proc. of Advances in Neural Information Processing System, 2002. 2
2. A. Bar-Hillel, T. Hertz, and D. Weinshall. Object class recognition by boosting a part-based model. In Proc. of IEEE International Conference on CVPR, 2005. 6, 7
3. Y. Chen, J. Bi, and Z. Wang. Miles: Multiple-instance learning via embedded instance selection. IEEE Transaction on Pattern Analysis and Machine Learning, 28(12):1931-1947,2006. 1, 2, 6, 7
4. Y. Chen and J. Z. Wang. Image categorization by learning and reasoning with regions. Journal of Machine Learning Research, 5:913-939, 2004. 2, 6, 7
5. T. Evgeniou, M. Pontil, and T. Poggio. Regularization networks and support vector machines. Advances in Computational Mathematics, 13(1):1-50, 2000. 2
6. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In Proc. of IEEE International Conference on Computer Vision and Pattern Recognition, pages 264-271, 2003. 6, 7
7. T. Gartner, P. A. Flach, A. Kowalczyk, and A. J. Smola. Multi-instance kernels, pages 179-186, 2002.
8. J. A. Hanley and B. J. McNeil. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology, 143:29-36, 1982. 6
9. T. Kadir and M. Brady. Scale, saliency and image description. International Journal of Computer Vision, 45(2):83-105, 2001. 7
10. E. Kofidis and P. A. Regalia. On the best rank-1 approximation of higher-order super-symmetric tensors. SIAM Journal on Matrix Analysis and Applications, 23(3):863-884, 2002. 1, 3
11. D. C. Liu and J. Nocedal. On the limited memory BFGS method for large scale optimization. Mathematical Programming B, 45(1-3):503-528, 1989. 5
12. O. Maron and A. L. Ratan. Multiple-instance learning for natural scene classification. In Proc. International Conference on Machine Learning, pages 341-349, 1998. 1, 2, 3
13. A. Opelt, M. Fussenegger, and A. Pinz. Weak hypotheses and boosting for generic object detection and recognition. In Proc. of ECCV, 2004. 6, 7
14. S. Ray and M. Craven. Supervised versus multiple instance learning: An empirical comparison. In Proc. International Conference on Machine Learning, pages 697-704, 2005. 2
15. B. Schölkopf, R. Herbrich, and A. J. Smola. A generalized représenter theorem. In Proc. of the Annual Conference on Computational Learning Theory, pages 416-426, 2001. 2, 5
16. A. Shashua, R. Zass, and T. Hazan. Multi-way clustering using super-symmetric non-negative tensor factorization. In Proc. of European Conference on Computer Vision, pages 595-608, 2006. 4
17. P. Viola, J. C. Piatt, and C. Zhang. Multiple instance boosting for object detection. In Proc. of Advances in Neural Information Processing System, 2005. 1, 2
18. R. Yager. On a general class of fuzzy connectives. Fuzzy Sets and Systems, 4:235-242, 1980. 3, 4
19. C. Yang, M. Dong, and J. Hua. Region-based image annotation using asymmetrical support vector machine-based multi-instance learning. In Proc. of IEEE International Conference on CVPR, 2006. 1, 2, 6, 7
20. Q. Zhang and S. A. Goldman. Em-dd: An improved multiple-instance learning technique. In Proc. of Advances in Neural Information Processing System, 2001. 2, 6, 7