Non-metric Similarity Ranking for Image Retrieval

  • Guang-Ho Cha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4080)


Over many years, almost all research work in the content-based image retrieval has used Minkowski distance (or L p -norm) to measure similarity between images. However such functions cannot adequately capture the aspects of the characteristics of the human visual system. In this paper, we present a new similarity measure reflecting the nonlinearity of human perception. Based on this measure, we develop a similarity ranking algorithm for effective image retrieval. This algorithm exploits the inherent cluster structure revealed by an image dataset. Our method yields encouraging experimental results on a real image database and demonstrates its effectiveness.


Image Retrieval Query Image Relevance Feedback Query Point Ranking Algorithm 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goh, K.-S., Li, B., Chang, E.: DynDex: A Dynamic and Non-metric Space Indexer. In: Proc. ACM Multimedia, pp. 466–475 (2002)Google Scholar
  2. 2.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Maxmillan, New York (1994)MATHGoogle Scholar
  3. 3.
    Ishikawa, Y., Subramanya, R., Faloutsos, C.: MindReader: Querying databases through multiple examples. In: Proc. VLDB Conf., pp. 218–227 (1998)Google Scholar
  4. 4.
    Muneesawang, P., Guan, L.: An Interactive Approach for CBIR Using a Network of Radial Basis Functions. IEEE Trans. on Multimedia 6(5), 703–716 (2004)CrossRefGoogle Scholar
  5. 5.
    Ng, A.Y., Jordan, M.I., Weiss, Y.: On Spectral Clustering: Analysis and Algorithm. In: Advances in Neural Information Processing Systems, vol. 14. MIT Press, Cambridge (2002)Google Scholar
  6. 6.
    Porkaew, K., Chakrabarti, K.: Query refinement for multimedia similarity retrieval in MARS. In: Proc. ACM Multimedia, pp. 235–238 (1999)Google Scholar
  7. 7.
    Rui, Y., et al.: Relevance feedback: A Power tool for interactive content-based image retrieval. IEEE Trans. Circuits and Video Technology 8(5), 644–644 (1998)CrossRefGoogle Scholar
  8. 8.
    Rui, Y., Huang, T., Mehrotra, S.: Content-based image retrieval with relevance feedback in MARS. In: Proc. Int’l Conf. on Image Processing (1997)Google Scholar
  9. 9.
    Schölkopf, B., Smola, A., Müller, K.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10, 1299–1319 (1998)CrossRefGoogle Scholar
  10. 10.
    Schölkopf, B., et al.: Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers. IEEE Trans. on Signal Processing 45, 2758–2765 (1997)CrossRefGoogle Scholar
  11. 11.
    Shrager, J., Hogg, T., Huberman, B.A.: Observation of phase transitions in spreading activation networks. Science 236, 1092–1094 (1987)CrossRefGoogle Scholar
  12. 12.
    Tong, S., Chang, E.: Support Vector Machine Active Learning for Image Retrieval. In: Proc. ACM Multimedia Conf., pp. 107–118 (2001)Google Scholar
  13. 13.
    De Valois, R.L., De Valois, K.K.: Spatial Vision. Oxford Science Publications, Oxford (1988)Google Scholar
  14. 14.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)MATHGoogle Scholar
  15. 15.
    Wu, L., Faloutsos, C., Sycara, K., Payne, T.R.: FALCON: Feedback Adaptive Loop for Content-Based Retrieval. In: Proc. of VLDB Conf., pp. 297–306 (2000)Google Scholar
  16. 16.
    Zhou, D., et al.: Learning with Local and Global Consistency. In: Advances in Neural Information Processing Systems, vol. 16. MIT Press, Cambridge (2004)Google Scholar
  17. 17.
    Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Technical report CMU-CALD-02-107, CMU (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guang-Ho Cha
    • 1
  1. 1.Department of Computer EngineeringSeoul National University of TechnologySeoulSouth Korea

Personalised recommendations