Multi-graph enabled active learning for multimodal web image retrieval

MIR 2005 - Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, Co-located with ACM Multimedia 2005

Volumn , Issue , 2005, Pages 65-72

  Wang, Xin Jing ^a   Li, Xing ^a   Ma, Wei Ying ^b   Zhang, Lei ^b  

^a TSINGHUA UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

Active learning; Graph learning; Multimodal image retrieval

Indexed keywords

ARTIFICIAL INTELLIGENCE; GRAPHIC METHODS; HYPERTEXT SYSTEMS;

ACTIVE LEARNING; GRAPH LEARNING; HETEROGENEOUS DATA; LEARNING PROBLEM; MULTI-MODAL IMAGE; TEXTUAL ANNOTATIONS; TRANSDUCTIVE LEARNING; TSINGHUA UNIVERSITY;

IMAGE RETRIEVAL;

EID: 78650181880     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1101826.1101839     Document Type: Conference Paper

References (25)

1. Barnard, K., Forsyth, D. Learning the Semantic of Words and Pictures. ICCV (2001).
2. Barnard K., Duygulu P., and Forsyth D. Clustering Art. Computer Vision and Pattern Recognition, pp. II:434-439, 2001
3. Belkin, M., Niyogi, P., Sindhwani, V. On Manifold Regularization. AISTATS, (2005)
4. Cai, D., He, X.F., Li, Z.W., Ma, W.-Y., and Wen, J.-R., Hierarchical Clustering of WWW Image Search Results Using Visual, Textual and Link Analysis. ACM Multimedia (2004)
5. Cai, D., Yu, S.P., Wen, J.-R., Ma, W.-Y. VIPS: a Visionbased Page Segmentation Algorithm. Microsoft Technical Report (MSR-TR-2003-79),(2003)
6. Chen, Z., Liu, W.Y., Zhang, F., Li, M.J. and Zhang, H.J. Web Mining for Web Image Retrieval, Journal of the American Society for Information Science and Technology, 52(10), (2001), 831-839.
7. Getoor, L. Link Mining: A New Data Mining Challenge. SIGKDD Explorations, volume 5, issue 1, 2003.
8. Google. http://image.google.com (2005)
9. He, J.R., Li, M.J., Zhang, H.-J. Tong, H.H., Zhang, C.S. Manifold-Ranking Based Image Retrieval. ACM Multimedia. (2004)
10. He, X.F, Yan, S.C., Hu, Y.X., Niyogi, P., and Zhang, H.-J., Face Recognition Using Laplacianfaces. IEEE Trans. on PAMI, vol.27, No.3, (Mar.2005)
11. Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J. and Zabih R. Image Indexing Using Color Correlograms, In Proc. IEEE Conference on CVPR., (1997) 762-768
12. Joachims, T., Making Large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning. Scholkopf B. and Burges C. and Smola A. (ed.), MIT-Press, 1999
13. Kumar, R., Raghavan, P., Rajagopalan, S., Sivakumar, D., Tomkins, A., and Upfal, E. The Web As a Graph, In Proc.th ACM SIGACT-SIGMOD-AIGART Symp. Principles of Database Systems, Publ., Dordrecht, 2002
14. Platt J.C., Smola A., Bartlett P., Schölkopf B., and Schuurmans D., Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, MIT Press, 1999, 61-74
15. Rui, Y., Huang, T.S., Mehrotra, S. Content-Based Image Retrieval with Relevance Feedback in MARS. ICIP, (1997)
16. Salton, G., and Buckley, C. Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management, 24(5). (1988). 513-523.
17. Salton G., and McGill M. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.
18. Tong, S., Chang, E., Support Vector Machine Active Learning for Image Retrieval. ACM Multimedia, (2001)
19. Vapnik V.. The Nature of Statistical Learning Theory. Springer Verlag, New York, (Sep. 1995)
20. th ACM International Conference on Multimedia, New York City, USA, Oct. 2004.
21. Westerveld, T. Probabilistic Multimedia Retrieval. SIGIR (2002)
22. Yahoo!, http://images.search.yahoo.com (2005)
23. Zhou, D., Bousquet, O., Lal, T.N., Weston, J., and Schölkopf, B., Learning with Local and Global Consistency. NIPS 16, (2004)
24. Zhou, D., Schölkopf, B., and Hofmann, T. Semi-supervised Learning on Directed Graphs. NIPS 17 (2005)
25. Zhou, D., Weston, J., Gretton, A., Bousquet O., and Schölkopf, B. Ranking on Data Manifolds. NIPS 16, (2004)