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.


Semantic Concept Semantic Object Single Descriptor Representative Block Elementary Building Block 
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.
    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)zbMATHGoogle 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