Retrieval of difficult image classes using svd-based relevance feedback

ABSTRACT

User-defined classes in large generalist image databases are often composed of several groups of images and span very different scales in the space of low-level visual descriptors. The interactive retrieval of such image classes is then very difficult. To addess his challenge, we propose and evaluate here two general mprovements of SVM-based relevance feedback methods. First, to optimize the transfer of information between the user and the system, we focus on the criterion employed by the system for selecting the images presented to the user at every feedback round. We put forward a new active learning selection criterion that minimizes redundancy between the candidate images shown to the user. Second, for image classes having very different scales, we find that a high sensitivity of the SVM to the scale of the data brings about a low retrieval performance. We then argue that insensitivity to scale is desirable in this context and we show how to obtain it by the use of specific kernel functions. The experimental evaluation of both ranking and classification performance on several image databases confirms the effectiveness of our selection criterion and of the use of kernels that reduce the sensitivity of SVMs to the scale of the data

REFERENCES

• 1: C. Berg, J. P. R. Christensen, and P. Ressel. Harmonic Analysis on Semigroups. Springer-Verlag, 1984.
• 2: N. Boujemaa, J. Fauqueur, M. Ferecatu, F. Fleuret, V. Gouet, B. L. Saux, and H. Sahbi. Ikona: Interactive generic and specific image retrieval. In Proceedings of the International workshop on Multimedia Content-Based Indexing and Retrieval (MMCBIR'2001), 2001.
• 3: K. Brinker. Incorporating diversity in active learning with support vector machines. In Proceedings of ICML-04, International Conference on Machine Learning, pages 59--66, August 2003.
• 4: Colin Campbell , Nello Cristianini , Alex J. Smola, Query Learning with Large Margin Classifiers, Proceedings of the Seventeenth International Conference on Machine Learning, p.111-118, June 29-July 02, 2000
• 5: E. Y. Chang, B. Li, G. Wu, and K. Goh. Statistical learning for effective visual image retrieval.In roceedings of the IEEE International Conference on Image Processing (ICIP'03), pages 609--612, September 2003.
• 6: O. Chapelle, P. Haffner, and V. N. Vapnik. Support-vector machines for histogram-based image classification. IEEE Transactions on Neural Networks, 10(5):1055--1064, 1999.
• 7: D. A. Cohn, Z. Ghahramani, and M. I. Jordan. Active learning with statistical models. Journal of Artificial Intelligence Research, 4:129--145, 1996.
• 8: I. J. Cox, M. L. Miller, T. P. Minka, T. Papathomas, and P. N. Yianilos. The Bayesian image retrieval system, PicHunter: theory, implementation and psychophysical experiments. IEEE Transactions on Image Processing, 9(1):20--37, January 2000.
• 9: I. J. Cox , M. L. Miller , T. P. Minka , P. N. Yianilos, An Optimized Interaction Strategy for Bayesian Relevance Feedback, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.553, June 23-25, 1998
• 10: F. Fleuret and H. Sahbi. Scale-invariance of support vector machines based on the triangular kernel. In 3rd International Workshop on Statistical and Computational Theories of Vision, October 2003.
• 11: T. Gevers and A. W. M. Smeulders. Content-based image retrieval: An overview. In G. Medioni and S. B. Kang, editors, Emerging Topics in Computer Vision. Prentice Hall, 2004.
• 12: Ralf Herbrich , Thore Graepel , Colin Campbell, Bayes point machines, The Journal of Machine Learning Research, 1, p.245-279, 9/1/2001
• 13: F. Jing, M. Li, L. Zhang, H.-J. Zhang, and B. Zhang. Learning in region-based image retrieval. In Proceedings of the IEEE International Symposium on Circuits and Systems, 2003.
• 14: B. Schoöolkopf. The kernel trick for distances. In Advances in Neural Information Processing Systems, volume 12, pages 301--307. MIT Press, 2000.
• 15: B. Schoöand A. Smola. Learning with Kernels. MIT Press, 2002.
• 16: Simon Tong , Edward Chang, Support vector machine active learning for image retrieval, Proceedings of the ninth ACM international conference on Multimedia, September 30-October 05, 2001, Ottawa, Canada [doi>10.1145/500141.500159]
• 17: Simon Tong , Daphne Koller, Support Vector Machine Active Learning with Application sto Text Classification, Proceedings of the Seventeenth International Conference on Machine Learning, p.999-1006, June 29-July 02, 2000
• 18: Vladimir Vapnik, Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, 1982
• 19: Constantin Vertan , Nozha Boujemaa, Upgrading Color Distributions for Image Retrieval: Can We Do Better?, Proceedings of the 4th International Conference on Advances in Visual Information Systems, p.178-188, November 02-04, 2000
• 20: X. S. Zhou and T. S. Huang. Relevance feedback for image retrieval: a comprehensive review. Multimedia Systems, 8(6):536--544, 2003.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms, Experimentation, Performance

Keywords:
active learning, image retrieval, kernel function, reduction of redundancy, sample selection