Retrieval with knowledge-driven kernel design: An approach to improving SVM-based CBIR with relevance feedback

Proceedings of the IEEE International Conference on Computer Vision

Volumn II, Issue , 2005, Pages 1355-1362

  Wang, Lei ^a,c   Gao, Yan ^a   Chan, Kap Luk ^a   Xue, Ping ^a   Yau, Wei Yun ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

GRADIENT-BASED SEARCH METHODS; KNOWLEDGE-DRIVEN KERNEL DESIGN; REAL-TIME RETRIEVAL PROCESS; SVM-BASED IMAGE RETRIEVAL;

COMPUTATIONAL METHODS; CONTENT BASED RETRIEVAL; DATABASE SYSTEMS; FEEDBACK; IMAGE SEGMENTATION; PROBLEM SOLVING; REAL TIME SYSTEMS; VECTOR QUANTIZATION;

NONBIBLIOGRAPHIC RETRIEVAL SYSTEMS;

EID: 33745894658     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2005.208     Document Type: Conference Paper

References (12)

1. http://vision.ece.ucsb.edu/datasets/.
2. G. Guo, A. K. Jain, W.-Y. Ma, and H. Zhang. Learning similarity measure for natural image retrieval with relevance feedback. IEEE Transactions on Neural Networks, 13(4):811-820, 2002.
3. S. Haykin. Neural Networks A Comprehensive Foundation (Second Edition). Prentice Hall International, Inc, 1999.
4. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 1988.
5. B. Schölkopf, P. Simard, A. Smola, and V. Vapnik. Prior knowledge in support vector kernels. Proceedings of the 1997 conference on Advances In neural Information processIng systems 10, Denver, 1997.
6. M. Stricker and M. Orengo. Similarity of color images. Proceedings of SPIE Storage and Retrieval for Image and Video Databases, 2420:381-392, 1995.
7. D. Tao and X. Tang. Random sampling based SVM for relevance feedback image retrieval. Proceedings of IEEE International Conference on Computer Visiona and Pattern Recognition (CVPR), 2:647-652, 2004.
8. Q. Tian, P. Hong, and T. S. Huang. Update Relevant Image Weights for Content-Based Image Retrieval using Support Vector Machines. IEEE International Conference on Multi-media and Expo (II), pages 1199-1202, 2000.
9. 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.
10. L. Wang, K. L. Chan, and Z. Zhang. Bootstrapping SVM Active Learning by Incorporating Unlabelled Images for Image Retrieval. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2003.
11. L. Wang, P. Xue, and K. L. Chan. Incorporating Prior Knowledge into SVM for Image Retrieval. Proceedings of International Conference on Pattern Recognition (ICPR), UK, 2004.
12. 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.