Texture Retrieval Effectiveness Improvement Using Multiple Representations Fusion

  • Noureddine Abbadeni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


We propose a multiple representations approach to tackle the problem of content-based image retrieval effectiveness. Multiple representations is based on the use of multiple models or representations and make them cooperate to improve search effectiveness. We consider the case of homogeneous textures. Texture is represented using two different models: the well-known autoregressive model and a perceptual model based on perceptual features such as coarseness and directionality. In the case of the perceptual model, two viewpoints are considered: perceptual features are computed on original images and on the autocovariance function corresponding to original images. Thus, we use a total of three representations (models and viewpoints) to represent texture content. Simple results fusion models are used to merge search results returned by each of the three representations. Benchmarking carried out on the well-known Brodatz database using the recall graph is presented. Retrieval relevance (effectiveness) is improved in a very appreciable way with the fused model.


Image Retrieval Autoregressive Model Relevance Feedback Multiple Representation Perceptual Feature 
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.


  1. 1.
    Abbadeni, N., Alhichri, H.: Low-level invariant image retrieval based on results fusion. In: Proceedings of the IEEE ICME, Hannover-Germany (June 2008)Google Scholar
  2. 2.
    Abbadeni, N.: Texture Representation and Retrieval Using the Causal Autoregressive Model. In: Qiu, G., Leung, C., Xue, X.-Y., Laurini, R. (eds.) VISUAL 2007. LNCS, vol. 4781, pp. 559–569. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Abbadeni, N.: Perceptual meaning of the estimated parameters of the autoregressive Model. In: Proceedings of the International Conference of Image Processing, Genova-Italy, pp. 1164–1167. IEEE, Los Alamitos (2005)Google Scholar
  4. 4.
    Abbadeni, N.: Multiple representations, similarity matching, and results fusion for CBIR. Multimedia Systems Journal 10(5), 444–456 (2005)CrossRefGoogle Scholar
  5. 5.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Transactions on Computing Surveys 40(2), 60 (2008)Google Scholar
  6. 6.
    Abbadeni, N.: A new similarity matching measure: application to texture-based image retrieval. In: Proceedings of the 3rd International Workshop on Texture Analysis and Synthesis. IEEE, Nice-France (2003)Google Scholar
  7. 7.
    Abbadeni, N., Ziou, D., Wang, S.: Computational measures corresponding to perceptual textural features. In: Proceedings of the 15th International Conference on Pattern Recognition, Barcelona-Spain, pp. 3913–3916. IEEE, Los Alamitos (2000)Google Scholar
  8. 8.
    Lew, M., Sebe, N., Djeraba, C., Jain, R.: Content-Based Multimedia Information Retrieval: State of the art and challenges. ACM Transactions on Multimedia Computing, Communications, and Applications 26 (2006)Google Scholar
  9. 9.
    Belkin, N.J., Cool, C., Croft, W.B., Callan, J.P.: The effect of multiple query representation on information retrieval performance. In: Proceedings of the 16th International ACM SIGIR Conference, pp. 339–346 (1993)Google Scholar
  10. 10.
    Berretti, S., Del Bimbo, A., Pala, P.: Merging results for distributed content-based image retrieval. Multimedia Tools and Applications 24, 215–232 (2004)CrossRefGoogle Scholar
  11. 11.
    Del Bimbo, A.: Visual information retrieval. Morgan Kaufmann Publishers, San Francisco (1999)Google Scholar
  12. 12.
    Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1966)Google Scholar
  13. 13.
    Dunlop, M.D.: Time, relevance and interaction modeling for information retrieval. In: Proceedings of the International ACM SIGIR Conference, Philadelphia, USA, pp. 206–213 (1997)Google Scholar
  14. 14.
    French, J.C., Chapin, A.C., Martin, W.N.: An application of multiple viewpoints to content-based image retrieval. In: Proceeding of the ACM/IEEE Joint Conference on Digital Libraries, pp. 128–130 (May 2003)Google Scholar
  15. 15.
    Gower, J.C.: A general coefficient of similarity and some of its properties. Biometrics Journal 27, 857–874 (1971)CrossRefGoogle Scholar
  16. 16.
    Lee, J.H.: Analysis of multiple evidence combination. In: Proceedings of the ACM SIGIR Conference, Philadelphia, PA, USA, pp. 267–276 (1997)Google Scholar
  17. 17.
    Liu, F., Picard, R.W.: Periodicity, directionality and randomness: Wold features for image modeling and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(7), 722–733 (1996)CrossRefGoogle Scholar
  18. 18.
    Lu, Y., Hu, C., Zhu, X., Zhang, H., Yang, Q.: A unified framework for semantics and feature based relevance feedback in image retrieval systems. In: Proceedings of the 8th ACM International Conference on Multimedia, Marina Del Rey, CA, pp. 31–37 (2000)Google Scholar
  19. 19.
    Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence, special issue on Digital Libraries 18(8), 837–842 (1996)CrossRefGoogle Scholar
  20. 20.
    Muneesawang, P., Guan, L.: An interactive approach for CBIR using a network of radial basis functions. IEEE Transactions on Multimedia 6(5), 703–716 (2004)CrossRefGoogle Scholar
  21. 21.
    Payne, J.S., Hepplewhite, L., Stonham, T.J.: Texture, human perception, and information retrieval measures. In: Proceedings of the ACM SIGIR MF/IR Workshop (July 2000)Google Scholar
  22. 22.
    Rui, Y., Huang, T.S., Mehrota, S.: A Relevance feedback architecture for multimedia information retrieval systems. In: IEEE Workshop on Content-based Access of Image and Video Libraries, pp. 82–89 (1997)Google Scholar
  23. 23.
    Sun, Y., Ozawa, S.: Semantic-meaningful content-based image retrieval in wavelet domain. In: Proceedings of the 5th ACM International Workshop on Multimedia Information Retrieval (held in conjunction with ACM Multimedia), Berkeley, CA, pp. 122–129 (November 2003)Google Scholar
  24. 24.
    Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics 8(6), 460–472 (1978)CrossRefGoogle Scholar
  25. 25.
    Vogt, C.C., Cottrell, G.W.: Fusion via a linear combination of scores. Information Retrieval Journal 1, 151–173 (1999)CrossRefGoogle Scholar
  26. 26.
    Wu, S., Crestani, F.: Data Fusion with Estimated Weights. In: Proceedings of the International ACM Conference on Knowledge and Information Management (CKIM), McLean, Virginie, USA, November 4-9, pp. 648–651 (2002)Google Scholar
  27. 27.
    Zhou, X.S., Huang, T.S.: Relevance feedback for image retrieval: a comprehensive review. ACM Multimedia Systems Journal 8(6), 536–544 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Noureddine Abbadeni
    • 1
  1. 1.College of Engineering and ITAl-Ain University of Science and TechnologyAl-AinUAE

Personalised recommendations