Image retrieval with SVM active learning embedding Euclidean search

IEEE International Conference on Image Processing

Volumn 1, Issue , 2003, Pages 725-728

  Wang, Lei ^a   Chan, Kap Luk ^a   Tan, Yap Peng ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

RELEVANCE FEEDBACK (RF); SUPPORT VECTOR MACHINES (SVM);

COMPUTATIONAL METHODS; DATABASE SYSTEMS; DEMODULATION; FEEDBACK; HEURISTIC METHODS; MATRIX ALGEBRA; PERFORMANCE; PROBABILITY; SEMANTICS;

CONTENT BASED RETRIEVAL;

EID: 0345097466     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. G. Guo, A. K. Jain, W.-Y. Ma, and H. Zhang. Learning similarity measure for natural image retrieval with relevance feedback. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 1:731-736, 2001.
2. R. Herbrich, T. Graepel, and R. C. Williamson. The structure of version space, submitted to Journal of Machine Learning Research, 2001.
3. T. Joachims. Making large-scale support vector machine learning practical. B. Scholkopf, C. Surges, and A. Smola. Advances in Kernel Methods: Support Vector Machines, MIT Press, Cambridge, MA, December, 1998.
4. T. Minka. An image database browser that learns from user interaction. Master's Thesis of MIT, 1996.
5. Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 8:644-655, 1998.
6. 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 Transactions on Pattern Analysis and Machine Intelligence, 22(12): 1349-1380, December 2000.
7. M. Stricker and M. Orengo. Similarity of color images. Proceedings of SPIE Storage and Retrieval for Image and Video Databases, 2420:381-392, 1995.
8. S. Tong and E. Chang. Support vector machine active learning for image retrieval. Proceedings of ACM International Conference on Multimedia, pages 107-118, Ottawa, Canada, Oct, 2001.
9. N. Vasconcelos and A. Lippman. Learning from user feedback in image retrieval systems. Proceedings of Neural Information Processing Systems (NIPS), Denver, Colorado, 1999.
10. X. S. Zhou and T. S. Huang. Small sample learning during multimedia retrieval using biasmap. Proceedings of IEEE International Conference on Computer Visiona and Pattern Recognition (CVPR), Hawaii, Dec, 2001.