Evaluating multi-class multiple-instance learning for image categorization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4844 LNCS, Issue PART 2, 2007, Pages 155-165

  Xu, Xinyu ^a   Li, Baoxin ^a  

^a AZ 85281

Author keywords

Image categorization; Multi class multiple instance learning

Indexed keywords

COMPUTER VISION; DATABASE SYSTEMS; IMAGE CLASSIFICATION; PERSONNEL TRAINING; PHASE NOISE; PROBLEM SOLVING;

IMAGE CATEGORIZATION; MULTI-CLASS MULTIPLE-INSTANCE LEARNING;

LEARNING SYSTEMS;

EID: 38349018853     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-76390-1_16     Document Type: Conference Paper

References (21)

1. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multipleinstance learning. NIPS 15, 561-568 (1998)
2. Bi, J., Chen, Y., Wang, J.Z.: A sparse support vector machine approach to region-based image categorization. CVPR (1), 1121-1128 (2005)
3. Chang, C.-C., Lin, C-J.: LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm2001
4. Chapelle, O., Haffner, P., Vapnik, V.N.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Networks 10(5), 1055-1064 (1999)
5. Chen, Y., Wang, J.Z.: Image categorization by learning and reasoning with regions. Journal of Machine Learning Research 5, 913-939 (2004)
6. Chen, Y., Bi, J., Wang, J.Z.: MILES: Multiple-Instance Learning via Embedded Instance Selection. IEEE Trans, on PAMI. 28(12), 1931-1947 (2006)
7. Csurka, G., Bray, C., Dance, C., Fan, L.: Visual Categorization with Bags of Keypoints. In: ECCV 2004 Workshop on Statistical Learning in Computer Vision, pp. 59-74 (2004)
8. Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence 89, 31-71 (1997)
9. Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13(2), 415-425 (2002)
10. Lee, Y., Lin, Y., Wahba, G.: Multicategory support vector machines: theory, and application to the classification of microarray data and satellite radiance data. Journal of the American Statistical Association 99, 67-81 (2004)
11. Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans, on PAMI 25(9), 1075-1088 (2003)
12. Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. NIPS 10, 570-576 (1998)
13. Maron, O., Ratan, A.L.: Multiple-instance learning for natural scene classification. ICML, 341-349 (1998)
14. Rahmani, R., Goldman, S.A.: MISSL: multiple-instance semi-supervised learning. ICML, 705-712 (2006)
15. Schölkopf, B., Smola, A.J.: Learning with Kernels-Support Vector Machines, Regularization, Optimization and Beyond, pp. 211-214. MIT Press, Cambridge, MA (2002)
16. Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
17. Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries. IEEE Trans. on PAMI 23(9), 947-963 (2001)
18. Wang, L., Shen, X., Zheng, Y.F.: On L-I norm multi-class Support Vector Machines. In: Proc. 5th Intl. Conf. on Machine Learning and Applications (2006)
19. Weston, J., Watkins, C.: Multi-class support vector machines. In: Verleysen, M. (ed.) Proc. ESANN 1999, D. Facto Press, Brussels (1999)
20. Zhang, Q., Goldman, S.A.: EM-DD: An improved multiple-instance learning technique. NIPS 14, 1073-1080 (2002)
21. Zhang, Q., Goldman, S.A., Yu, W., Fritts, J.E.: Content-based image retrieval using multiple-instance learning. In: Proc. ICML, pp. 682-689 (2002)