Dynamic Feature Space Selection in Relevance Feedback Using Support Vector Machines

Fang Qian, Mingjing Li, Lei Zhang, Hongjiang Zhang, Bo Zhang

Abstract

The selection of relevant features plays a critical role in relevance feedback for content-based image retrieval. In this paper, we propose an approach for dynamically selecting the most relevant feature space in relevance feedback. During the feedback process, an SVM classifier is constructed in each feature space, and its generalization error is estimated. The feature space with the smallest generalization error is chosen for the next round of retrieval. Several kinds of estimators are discussed. We demonstrate experimentally that the prediction of the generalization error of SVM classifier is effective in relevant feature space selection for content-based image retrieval.

References

  1. Blum, A. and Langley, P. (1997) Selection of relevant features and examples in machine learning. Artificial Intelligence, 97:245-271
  2. Burges, C.J.C. (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):955-974
  3. Chang, E. and Li, B. (2001) MEGA --- The Maximizing Expected Generalization Algorithm for learning complex query concepts, February. UCSB Technical Report, http://wwwdb.stanford.edu/echang/mega-extended.pdf
  4. Chapelle, O., Vapnik, V., Bousquet, O. and Mukherjee, S. (2001) Choosing multiple parameters for support vector machines. Machine Learning
  5. Cox, I. J. , Minka, T. P., Papathomas, T.V. and Yianilos, P. N. (2000) The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments, IEEE Transactions on Image Processing - special issue on digital libraries
  6. Hong, P., Tian, Q. and Huang, T. S. (2000) Incorporate support vector machines to contentbased image retrieval with relevance feedback, IEEE Int'l conf. on Image Processing (ICIP'2000), Vancouver, Canada
  7. Ishikawa, Y. and Subramanya, R. (1998) MindReader: Query databases through multiple examples, in Proc. of the 24th VLDB conference, (New York)
  8. Joachims, T. (2000) Estimating the generalization performance of a SVM efficiently. Proceedings of the Seventeenth International Conference on Machine Learning. 2000. San Francisco: Morgan Kaufman
  9. Klinkenberg, R. and Joachims, T. (2000) Detecting concept drift with support vector machines. In Proceedings of the Seventeenth International Conference on Machine Learning. 2000. San Francisco. Morgan Kaufmann
  10. Manjunath, B.S., and Ma, W.Y., Texture features for browsing and retrieval of large image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 837-842, 1996
  11. Qian, F., Li, M., Ma, W., Lin, F. and Zhang, B. (2001) Alternating feature spaces in relevance feedback, 3rd International Workshop on Multimedia Information Retrieval, October 5, 2001, Ottawa, Canada
  12. Rui Y. and Huang T. S. (2000) Optimizing learning in image retrieval. Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), Hilton Head, SC
  13. Rui, Y., Huang, T. S., Ortega, M. and Mehrotra, S. (1998) Relevance feedback: A power tool in interactive content-based image retrieval, IEEE Transaction on Circuits and Systems for Video Technology, Special Issue on Segmentation, Description, and Retrieval of Video Content, 8(5): 644-655
  14. Stricker M. and Orengo, M., Similarity of color images. in Proc. SPIE Storage and Retrieval for Image and Video Databases. 1995
  15. Su, Z., Zhang, H. J. and Ma, S. (2001) Relevant feedback using a Bayesian classifier in content-based image retrieval, SPIE Electronic Imaging 2001, San Jose, CA
  16. Tieu, K. and Viola, P. (2000) Boosting image retrieval, Proc. IEEE Conf. Computer Vision and Pattern Recognition, Hilto Head Island, SC
  17. Tong, S. and Chang, E. (2001) Support vector machine active learning for image retrieval, in Proc. ACM Multimedia 2001, Ottawa, Canada
  18. Vapnik, V. (1995) The nature of statistical learning theory. Springer-Verlag, New York
  19. Vapnik, V. (1998) Statistical learning theory. Chichester, GB: Wiley
  20. Vapnik, V. and Chapelle, O. (2000) Bounds on error expectation for support vector machines. Neural Computation
  21. Vasconcelos, N. and Lippman, A. (1999) Learning from user feedback in image retrieval systems, NIPS'99, Denver, Colorado
  22. Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T. and Vapnik, V. (2001) Feature selection for SVMs. In Sara A Solla, Todd K Leen, and Klaus-Robert Muller, editors, Advances in Neural Information Processing Systems 13. MIT Press
  23. Wu, Y., Tian, Q. and Huang, T. S. (2000) Discriminant-EM algorithm with application to image retrieval, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, South Carolina
  24. Zhang, L., Lin, F. and Zhang, B. (1999) A CBIR method based on color-spatial feature. IEEE Region 10 Annual International Conference 1999 (TENCON'99), Cheju, Korea. 1999:166-169
  25. Zhang, L., Lin, F. and Zhang, B. (2001a) Support vector learning for image retrieval, IEEE International Conference on Image Processing (ICIP 2001). pp721-724. Thessaloniki, Greece
  26. Zhang, L., Lin, F. and Zhang, B. (2001b) A neural network based self-learning algorithm of image retrieval, Chinese Journal of Software, 12(10): 1479-1485
Download


Paper Citation


in Harvard Style

Qian F., Li M., Zhang L., Zhang H. and Zhang B. (2004). Dynamic Feature Space Selection in Relevance Feedback Using Support Vector Machines . In Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004) ISBN 972-8865-01-5, pages 186-195. DOI: 10.5220/0002674001860195


in Bibtex Style

@conference{pris04,
author={Fang Qian and Mingjing Li and Lei Zhang and Hongjiang Zhang and Bo Zhang},
title={Dynamic Feature Space Selection in Relevance Feedback Using Support Vector Machines},
booktitle={Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004)},
year={2004},
pages={186-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002674001860195},
isbn={972-8865-01-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004)
TI - Dynamic Feature Space Selection in Relevance Feedback Using Support Vector Machines
SN - 972-8865-01-5
AU - Qian F.
AU - Li M.
AU - Zhang L.
AU - Zhang H.
AU - Zhang B.
PY - 2004
SP - 186
EP - 195
DO - 10.5220/0002674001860195