Semi-supervised distance metric learning for collaborative image retrieval

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Hoi, Steven C H ^a   Liu, Wei ^b   Chang, Shih Fu ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b Department of Earth and Environmental Sciences

Author keywords

[No Author keywords available]

Indexed keywords

CONTENT-BASED IMAGE RETRIEVAL; DISTANCE METRIC LEARNING; EUCLIDEAN METRIC; LAPLACIAN; LOG DATA; METRIC LEARNING; PROMISING APPROACH; REGULARIZATION FRAMEWORK; RELEVANCE FEEDBACK; ROBUST DISTANCE; SEARCH PERFORMANCE; SEMANTIC GAPS; SEMI-SUPERVISED; SIDE INFORMATION; STANDARD METRICS; UNLABELED DATA;

ARTIFICIAL INTELLIGENCE; CHANNEL ESTIMATION; COMPUTER VISION; CONTENT BASED RETRIEVAL; CONTROL THEORY; EDUCATION; FEATURE EXTRACTION; FEEDBACK; IMAGE PROCESSING; INFORMATION RETRIEVAL; INFORMATION THEORY; LEARNING ALGORITHMS; PATTERN RECOGNITION; STANDARDS;

IMAGE RETRIEVAL;

EID: 51949106897     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587351     Document Type: Conference Paper

References (24)

1. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall. Learning a mahalanobis metric from equivalence constraints. JMLR, 6:937-965, 2005.
2. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2003.
3. T. Cox and M. Cox. Multidimensional Scaling. Chapman & Hall, London, 1994.
4. L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding, 106(1):59-70, 2007.
5. K. Fukunaga. Introduction to Statistical Pattern Recognition. Elsevier, 1990.
6. F. Girosi, M. Jones, and T. Poggio. Regularization theory and neural networks architectures. Neural Computation, 7:219-269, 1995.
7. A. Globerson and S. Roweis. Metric learning by collapsing classes. In NIPS'05, 2005.
8. X. He, W.-Y. Ma, and H.-J. Zhang. Learning an image manifold for retrieval. In ACM Multimedia, pages 17-23, New York, 2004.
9. C.-H. Hoi and M. R. Lyu. Biased support vector machine for relevance feedback in image retrieval. In Proc. Intl. Joint Conf. on Neural Networks (IJCNN'04), Budapest, Hungary, 2004.
10. C.-H. Hoi and M. R. Lyu. Group-based relevance feedback with support vector machine ensembles. In Proc. 17th International Conf. on Pattern Recognition (ICPR'04), volume 3, pages 874-877, Cambridge, UK, 2004.
11. S. C. H. Hoi, W. Liu, M. R. Lyu, and W.-Y. Ma. Learning distance metrics with contextual constraints for image retrieval. In Proc. CVPR2006, New York, US, June 17-22 2006.
12. S. C. H. Hoi, M. R. Lyu, and R. Jin. Integrating user feedback log into relevance feedback by coupled svm for content-based image retrieval. In Proc. International Conference on Data Engineering Workshops, 2005.
13. S. C. H. Hoi, M. R. Lyu, and R. Jin. A unified log-based relevance feedback scheme for image retrieval. IEEE Trans. KDE, 18(4):509-204, 2006.
14. G. H. J. Goldberger, S. Roweis and R. Salakhutdinov. Neighbourhood components analysis. In NIPS17, 2005.
15. B. Manjunath, P. Wu, S. Newsam, and H. Shin. A texture descriptor for browsing and similarity retrieval. Signal Processing Image Communication, 2001.
16. S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, 2000.
17. L. Si, R. Jin, S. C. H. Hoi, and M. R. Lyu. Collaborative image retrieval via regularized metric learning. ACM Multimedia Systems Journal, 12(1):34-44, 2006.
18. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. PAMI, 22(12):1349-1380, 2000.
19. J. F. Sturm. Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones. Optimization Methods and Software, 11-12:625-653, 1999.
20. J. B. Tenenbaum and V. de Silva andJohn C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, 2000.
21. V. N. Vapnik. Statistical Learning Theory. John Wiley & Sons, 1998.
22. K. Weinberger, J. Blitzer, and L. Saul. Distance metric learning for large margin nearest neighbor classification. In NIPS 18, pages 1473-1480, 2006.
23. E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell. Distance metric learning with application to clustering with side-information. In NIPS2002, 2002.
24. L. Yang, R. Jin, R. Sukthankar, and Y. Liu. An efficient algorithm for local distance metric learning. In AAAI2006, 2006.