Locally smooth metric learning with application to image retrieval

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2007, Pages

  Chang, Hong ^a   Yeung, Dit Yan ^b  

^a XEROX RESEARCH CENTRE EUROPE   (France)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPUTER NETWORKS; COMPUTER VISION; CONTENT BASED RETRIEVAL; CURVE FITTING; DATA STRUCTURES; IMAGE PROCESSING; INFORMATION RETRIEVAL; LEAST SQUARES APPROXIMATIONS; STRUCTURAL OPTIMIZATION;

METRIC LEARNING;

EDUCATION;

EID: 50649092396     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2007.4408862     Document Type: Conference Paper

References (16)

1. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall. Learning distance functions using equivalence relations. In Proceedings of the Twentieh International Conference on Machine Learning, pages 11-18, 2003.
2. H. Chang and D. Yeung. Locally linear metric adaptation for semi-supervised clustering. In Proceedings of the Twenty-First International Conference on Machine Learning, pages 153-160, 2004.
3. C. Domeniconi, J. Peng, and D. Gunopulos. Locally adaptive metric nearest-neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9):1281-1285, 2002.
4. J. Friedman. Flexible metric nearest neighbor classification. Technical report, Department of Statistics, Stanford University, Stanford, CA, USA, November 1994.
5. J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov. Neighbourhood component analysis. In Advances in Neural Information Processing Systems 17, pages 513-520. 2005.
6. T. Hastie and R. Tibshirani. Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6):607-616, 1996.
7. T. Hertz, A. Bar-Hillel, and D. Weinshall. Learning distance functions for image retrieval. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2, pages 570-577, 2004.
8. D. Levin. The approximation power of moving least squares. Mathematics of Computation, 67(224):1517-1531, 1998.
9. G. Pass, R. Zabih, and J. Miller. Comparing images using color coherence vectors. In Proceedings of the Fourth ACM International Conference on Multimedia, 1996.
10. W. Rand. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66:846-850, 1971.
11. S. Schaefer, T. McPhail, and J. Warren. Image deformation using moving least squares. In Proceedings of SIGGRAPH 2006, 2006.
12. R. Short and K. Fukunaga. The optimal distance measure for nearest neighbor classification. IEEE Transactions on Information Theory, 27(5):622-627, 1981.
13. K. Weinberger, J. Blitzer, and L. Saul. Distance metric learning for large margin nearest neighbor classification. In Advances in Neural Information Processing Systems 18. 2006.
14. E. Xing, A. Ng, M. Jordan, and S. Russell. Distance metric learning, with application to clustering with sideinformation. In Advances in Neural Information Processing Systems 15. 2003.
15. Z. Zhang, J. Kwok, and D. Yeung. Parametric distance metric learning with label information. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pages 1450-1452, 2003.
16. X. Zhu. Semi-supervised learning with graphs. PhD thesis, Carnegie Mellon University, 2005. CMU-LTI-05-192.