Advertisement

Score Distribution Approach to Automatic Kernel Selection for Image Retrieval Systems

  • Anca Doloc-Mihu
  • Vijay V. Raghavan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)

Abstract

This paper introduces a kernel selection method to automatically choose the best kernel type for a query by using the score distributions of the relevant and non-relevant images given by user as feedback. When applied to our data, the method selects the same best kernel (out of the 12 tried kernels) for a particular query as the kernel obtained from our extensive experimental results.

Keywords

Image Retrieval Score Distribution Relevant Image Kernel Type Relevance Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Doloc-Mihu, A., Raghavan, V.V., Bollmann-Sdorra, P.: Color Retrieval in Vector Space Model. In: Proceedings of the 26th International ACM SIGIR Workshop on Mathematical/Formal Methods in Information Retrieval, MF/IR (2003)Google Scholar
  2. 2.
    Raghavan, V.V., Wong, S.K.M.: A Critical Analysis of Vector Space Model for Information Retrieval. Journal of the American Society for Information Science 37, 279–287 (1986)Google Scholar
  3. 3.
    Bollmann-Sdorra, P., Jochum, F., Reiner, U., Weissmann, V., Zuse, H.: The LIVE Project - Retrieval Experiments Based on Evaluation Viewpoints. In: Proceedings of the 8th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 213–214 (1985)Google Scholar
  4. 4.
    Chapelle, O., Haffner, P., Vapnik, V.: SVMs for Histogram-Based Image Classification. IEEE Transactions on Neural Networks 5, 1055–1064 (1999)CrossRefGoogle Scholar
  5. 5.
    Huijsmans, D.P., Sebe, N.: How to Complete Performance Graphs in Content-Based Image Retrieval: Add Generality and Normalize Scope. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 245–251 (2005)CrossRefGoogle Scholar
  6. 6.
    Doloc-Mihu, A., Raghavan, V.V.: Selecting the Kernel Type for a Web-based Adaptive Image Retrieval System (AIRS). In: Internet Imaging VII, Proceedings of SPIE-IS&T Electronic Imaging, SPIE, vol. 6061 (2006)Google Scholar
  7. 7.
    Doloc-Mihu, A., Raghavan, V.V.: Using Score Distribution Models to Select the Kernel Type for a Web-based Adaptive Image Retrieval System (AIRS). In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds.) CIVR 2006. LNCS, vol. 4071, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Swets, J.A.: Information Retrieval Systems. Science 141, 245–250 (1963)CrossRefGoogle Scholar
  9. 9.
    Manmatha, R., Feng, F., Rath, T.: Using Models of Score Distributions in Information Retrieval. In: Proceedings of the 24th ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 267–275 (2001)Google Scholar
  10. 10.
    Arampatzis, A., Beney, J., Koster, C., van der Weide, T.P.: Incrementally, Half-Life, and Threshold Optimization for Adaptive Document Filtering. In: The 9th Text REtrieval Conference (TREC-9) (2000)Google Scholar
  11. 11.
    Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)Google Scholar
  12. 12.
    Tipping, M.E.: The Relevance Vector Machine. Advances in Neural Information Processing Systems 12, 652–658 (2000)Google Scholar
  13. 13.
    Zhang, Y., Callan, J.: Maximum Likelihood Estimation for Filtering Thresholds. In: Proceedings of the 24th ACM SIGIR Conference on Research and Development in Information Retrieval (2001)Google Scholar
  14. 14.
    Smith, J.R.: Integrated Spatial and Feature Image Systems: Retrieval, Analysis and Compression. PhD thesis, Columbia University (1997)Google Scholar
  15. 15.
    Heckbert, P.S.: Color Image Quantization for Frame Buffer Display. Proceedings of SIGGRAPH 16, 297–307 (1982)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Anca Doloc-Mihu
    • 1
  • Vijay V. Raghavan
    • 1
  1. 1.University of Louisiana at LafayetteLafayetteUSA

Personalised recommendations