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A Bayesian Network Approach to Multi-feature Based Image Retrieval

  • Qianni Zhang
  • Ebroul Izquierdo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4306)

Abstract

This paper aims at devising a Bayesian Network approach to object centered image retrieval employing non-monotonic inference rules and combining multiple low-level visual primitives as cue for retrieval. The idea is to model a global knowledge network by treating an entire image as a scenario. The overall process is divided into two stages: the initial retrieval stage which is concentrated on finding an optimal multi-feature space stage and doing a simple initial retrieval within this space; and the Bayesian inference stage which uses the initial retrieval information and seeks for a more precise second- retrieval.

Keywords

Bayesian Network Image Retrieval Semantic Concept Elementary Block Bayesian Belief Network 
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.

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References

  1. 1.
    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
  2. 2.
    Mojsilovic: A computational model for color naming and describing color composition of images. IEEE Transactions on Image Progressing 14(5), 690–699 (2005)CrossRefGoogle Scholar
  3. 3.
    Fei-Fei, L., Fergus, R., Perona, P.: A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories. In: Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV 2003) (2003)Google Scholar
  4. 4.
    Hoiem, D., Sukthankar, R., Schneiderman, H., Huston, L.: Object-Based Image Retrieval Using the Statistical Structure of Images. In: IEEE Conference on Computer Vision and Pattern Recognition (June 2004)Google Scholar
  5. 5.
    Fergus, R., Perona, P., Zisserman, A.: Object Class Recognition by Unsupervised Scale-Invariant Learning. In: IEEE CVPR 2003 (2003)Google Scholar
  6. 6.
    Helmer, S., Lowe, D.G.: Object Class Recognition with Many Local Features. In: IEEE CVPRW 2004 (2004)Google Scholar
  7. 7.
    Vailaya, A., Figueiredo, M., Jain, A., Zhang, H.J.: A Bayesian Framework for Semantic Classification of Outdoor Vacation Images. In: Proc. SPIE: Storage and Retrieval for Image and Video Databases VII, San Jose, CA, January 1999, vol. 3656, pp. 415–426 (1999)Google Scholar
  8. 8.
    Luo, J., Savakis, A.: Indoor vs Outdoor Classification of Consumer Photographs Using Low-level and Semantic Features. In: IEEE, ICIP 2001, pp. 745–748 (2001)Google Scholar
  9. 9.
    Li, J., Wang, J.Z.: Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach. IEEE Transactions On Pattern Analysis and Machine Intelligence 25(9) (2003)Google Scholar
  10. 10.
    He, X., King, O., Ma, W.-Y., Li, M., Zhang, H.-J.: Learning a Semantic Space From User’s Relevance Feedback for Image Retrieval. IEEE Transactions On Circuits And Systems For Video Technology 13(1) (January 2003)Google Scholar
  11. 11.
    Adamsy, W.H., Iyengary, G., Linz, C.-Y., Naphadez, M.R., Netiy, C., Nocky, H.J., Smithz, J.R.: Semantic Indexing of Multimedia Content using Visual, Audio and Text cues. In: EURASIP JASP 2003 (2003)Google Scholar
  12. 12.
    Steuer, R.E.: Multiple criteria optimization. In: Theory, Computation, and Application. Wiley, New York (1986)Google Scholar
  13. 13.
    Knowles, J., Corne, D.: Approximating the non-dominated front using the Pareto Archived Evolution Strategy (1999)Google Scholar
  14. 14.
    Zhang, Q., Izquierdo, E.: A Multi-Feature Optimization Approach to Object-Based Image Classification. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds.) CIVR 2006. LNCS, vol. 4071, pp. 310–319. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    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
  17. 17.
    Swain, M., Ballard, D.: Color indexing. International Journal of Computer Vision (1991)Google Scholar
  18. 18.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, CA (2001)zbMATHGoogle Scholar
  19. 19.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., San Francisco (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qianni Zhang
    • 1
  • Ebroul Izquierdo
    • 1
  1. 1.Department of Electronic Engineering, Queen MaryUniversity of LondonLondonU.K.

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