Retrieval of difficult image classes using SVM-based relevance feedback

MIR'04 - Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval

Volumn , Issue , 2004, Pages 23-30

  Ferecatu, Marin ^a   Crucianu, Michel ^a   Boujemaa, Nozha ^a  

^a INRIA ROCQUENCOURT   (France)

Author keywords

Active learning; Image retrieval; Kernel function; Reduction of redundancy; Sample selection

Indexed keywords

DATABASE SYSTEMS; INTERACTIVE COMPUTER SYSTEMS; LEARNING SYSTEMS; SEARCH ENGINES; SEMANTICS; WORLD WIDE WEB;

ACTIVE LEARNING; KERNEL FUNCTIONS; REDUCTION OF REDUNDANCY; SAMPLE SELECTION;

IMAGE RETRIEVAL;

EID: 15344344969     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. C. Berg, J. P. R. Christensen, and P. Ressel. Harmonic Analysis on Semigroups. Springer-Verlag, 1984.
2. N. Boujemaa, J. Fauqueur, M. Ferecatu, F. Fleuret, V. Gouet, B. L. Saux, and H. Sahbi. Ikona: Interactive generic and specific image retrieval. In Proceedings of the International workshop on Multimedia Content-Based Indexing and Retrieval (MMCBIR'2001), 2001.
3. K. Brinker. Incorporating diversity in active learning with support vector machines. In Proceedings of ICML-04, International Conference on Machine Learning, pages 59-66, August 2003.
4. C. Campbell, N. Cristianini, and A. Smola. Query learning with large margin classifiers. In Proceedings of ICML-00, 17th International Conference on Machine Learning, pages 111-118. Morgan Kaufmann, 2000.
5. E. Y. Chang, B. Li, G. Wu, and K. Goh. Statistical learning for effective visual image retrieval. In Proceedings of the IEEE International Conference on Image Processing (ICIP'09), pages 609-612, September 2003.
6. O. Chapelle, P. Haffner, and V. N. Vapnik. Support-vector machines for histogram-based image classification. IEEE Transactions on Neural Networks, 10(5):1055-1064, 1999.
7. D. A. Cohn, Z. Ghahramani, and M. I. Jordan. Active learning with statistical models. Journal of Artificial Intelligence Research, 4:129-145, 1996.
8. I. J. Cox, M. L. Miller, T. P. Minka, T. Papathomas, and P. N. Yianilos. The Bayesian image retrieval system, PicHunter: theory, implementation and psychophysical experiments. IEEE Transactions on Image Processing, 9(1):20-37, January 2000.
9. I. J. Cox, M. L. Miller, S. M. Omohundro, and P. N. Yianilos. An optimized interaction strategy for Bayesian relevance feedback. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 553-558. IEEE Computer Society, 1998.
10. F. Fleuret and H. Sahbi. Scale-invariance of support vector machines based on the triangular kernel. In 3rd International Workshop on Statistical and Computational Theories of Vision, October 2003.
11. T. Gevers and A. W. M. Smeulders. Content-based image retrieval: An overview. In G. Medioni and S. B. Kang, editors, Emerging Topics in Computer Vision. Prentice Hall, 2004.
12. R. Herbrich, T. Graepel, and C. Campbell. Bayes point machines. Journal of Machine Learning Research, 1:245-279, 2001.
13. F. Jing, M. Li, L. Zhang, H.-J. Zhang, and B. Zhang. Learning in region-based image retrieval. In Proceedings of the IEEE International Symposium on Circuits and Systems, 2003.
14. B. Schölkopf. The kernel trick for distances. In Advances in Neural Information Processing Systems, volume 12, pages 301-307. MIT Press, 2000.
15. B. Schölkopf and A. Smola. Learning with Kernels. MIT Press, 2002.
16. S. Tong and E. Chang. Support vector machine active learning for image retrieval. In Proceedings of the ninth ACM international conference on Multimedia, pages 107-118. ACM Press, 2001.
17. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In Proceedings of ICML-00, 17th International Conference on Machine Learning, pages 999-1006. Morgan Kaufmann, 2000.
18. V. Vapnik. Estimation of dependencies based on empirical data. Springer Verlag, 1982.
19. C. Vertan and N. Boujemaa. Upgrading color distributions for image retrieval: can we do better? In International Conference on Visual Information Systems (Visual2000), November 2000.
20. X. S. Zhou and T. S. Huang. Relevance feedback for image retrieval: a comprehensive review. Multimedia Systems, 8(6):536-544, 2003.