A Multi-feature Optimization Approach to Object-Based Image Classification

  • Qianni Zhang
  • Ebroul Izquierdo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)


This paper proposes a novel approach for the construction and use of multi-feature spaces in image classification. The proposed technique combines low-level descriptors and defines suitable metrics. It aims at representing and measuring similarity between semantically meaningful objects within the defined multi-feature space. The approach finds the best linear combination of predefined visual descriptor metrics using a Multi-Objective Optimization technique. The obtained metric is then used to fuse multiple non-linear descriptors is be achieved and applied in image classification.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Smith, J.R., Chang, S.: Visualseek: a fully automated content-based image query system. In: Proceedings of ACM Multimedia, Boston, MA, USA, vol. 96, pp. 87–98 (1996)Google Scholar
  2. 2.
    Chang, S.-E., Sikora, T., Purl, A.: Overview of the MPEG-7 Standard. IEEE Transactions on Circuits and Systems for Video Technology 11(6), 688–695 (2001)CrossRefGoogle Scholar
  3. 3.
    O’Reilly, J.: ContentEengineering. Electronics Communications Engineering Journal 14(4) (August 2002)Google Scholar
  4. 4.
    Eidenberger, H., Breiteneder, C.: Macro-level Similarity Measurement in ViZir (2002)Google Scholar
  5. 5.
    Tian, Q., Wu, Y., Huang, T.S.: Combine User Defined Region-Of-Interest and Spatial Layout for Image Retrieval. In: IEEE ICIP 2000, vol. 3, pp. 746–749 (2000)Google Scholar
  6. 6.
    Yanai, K., Barnard, K.: Image Region Entropy: A Measure of “Visualness”of Web Images Associated with One Concept. In: Proc. ACM Multimedia, pp. 419–422 (2005)Google Scholar
  7. 7.
    Yan, R., Yang, J., Hauptmann, A.G.: Learning QueryClass Dependent Weights in Automatic Video Retrieval. In: Proc. ACM Multimedia, pp. 548–555 (2004)Google Scholar
  8. 8.
    Steuer, R.E.: Multiple Criteria Optimization: Theory, Computation, and Application. Wiley, New York (1986)MATHGoogle Scholar
  9. 9.
    Knowles, J., Corne, D.: Approximating the Non-dominated front using the Pareto Archived Evolution Strategy (1999)Google Scholar
  10. 10.
    Knowles, J., Corne, D.: Properties of an adaptive archiving algorithm for storing nondominated vectors (2002)Google Scholar
  11. 11.
    O’ Connor, N., Cooke, E., Le Borgne, H., Blighe, M., Adamek, T.: The aceToolbox: Lowe-Level AudioVisual Feature Extraction for Retrieval and Classification. In: Proc. of EWIMT 2005 (November 2005)Google Scholar
  12. 12.
    Manjunath, B.S., Ma, W.T.: Texture features for browsing and retrieval of image data. IEEE Trans. On Pattern Analysis and Machine Intelligence 18(8), 837–842 (1996)CrossRefGoogle Scholar
  13. 13.
    Tuceryan, M., Jain, A.K.: Texture Analysis. In: The Handbook of Pattern Recognition and Computer Vision, 2nd edn., pp. 207–248. World Scientific Publishing Co., Singapore (1998)Google Scholar
  14. 14.
    Swain, M.J.A., Ballard, D.H.A.: Color indexing. International Journal of Computer Vision 7, 11–32 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qianni Zhang
    • 1
  • Ebroul Izquierdo
    • 1
  1. 1.Queen MaryUniversity of LondonLondonUK

Personalised recommendations