A NSGA Based Approach for Content Based Image Retrieval

  • Salvador Moreno-Picot
  • Francesc J. Ferri
  • Miguel Arevalillo-Herráez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)


The purpose of CBIR (Content Based Image Retrieval) systems is to allow users to retrieve pictures related to a semantic concept of their interest, when no other information but the images themselves is available. Commonly, a series of images are presented to the user, who judges on their relevance. Several different models have been proposed to help the construction of interactive systems based on relevance feedback. Some of these models consider that an optimal query point exists, and focus on adapting the similarity measure and moving the query point so that it appears close to the relevant results and far from those which are non-relevant. This implies a strong causality between the low level features and the semantic content of the images, an assumption which does not hold true in most cases. In this paper, we propose a novel method that considers the search as a multi-objective optimization problem. Each objective consists of minimizing the distance to one of the images the user has considered relevant. Representatives of the Pareto set are considered as points of interest in the search space, and parallel searches are performed for each point of interest. Results are then combined and presented to the user. A comparatively good performance has been obtained when evaluated against other baseline methods.


Image Retrieval Relevance Feedback Query Point Content Base Image Retrieval Pareto Optimal Front 
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.
    Thomee, B., Lew, M.S.: Interactive search in image retrieval: a survey. International Journal of Multimedia Information Retrieval 1(2), 71–86 (2012)CrossRefGoogle Scholar
  2. 2.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40(2), 1–60 (2008)CrossRefGoogle Scholar
  3. 3.
    Ishikawa, Y., Subramanya, R., Faloutsos, C.: Mindreader: Querying databases through multiple examples. In: Proc. 24th Int. Conf. Very Large Data Bases, VLDB, New York, USA, pp. 433–438 (1998)Google Scholar
  4. 4.
    Rui, Y., Huang, S., Ortega, M., Mehrotra, S.: Relevance feeback: a power tool for interactive content-based image retrieval. IEEE Transaction on Circuits and Video Technology 8(5), 644–655 (1998)CrossRefGoogle Scholar
  5. 5.
    Ciocca, G., Schettini, R.: A relevance feedback mechanism for content-based image retrieval. Information Processing and Management 35(1), 605–632 (1999)CrossRefGoogle Scholar
  6. 6.
    de Freitas, N., Brochu, E., Barnard, K., Duygulu, P., Forsyth, D.: Bayesian models for massive multimedia databases: a new frontier. Technical Report TR-2003-5, Department of Computer Science, University of British Columbia (2003)Google Scholar
  7. 7.
    Vasconcelos, N., Lippman, A.: Learning from user feedback in image retrieval systems. In: Proceedings of Advances in Neural Information Processing Systems (NIPS 1999), Denver, Colo, USA, pp. 977–986 (November-December 1999)Google Scholar
  8. 8.
    Arevalillo-Herráez, M., Ferri, F.J., Domingo, J.: A naive relevance feedback model for content-based image retrieval using multiple similarity measures. Pattern Recognition 43(3), 619–629 (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Giacinto, G.: A nearest-neighbor approach to relevance feedback in content based image retrieval. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval (CIVR 2007), Amsterdam, The Netherlands, pp. 456–463. ACM Press (1993)Google Scholar
  10. 10.
    Arevalillo-Herraez, M., Ferri, F.J.: Interactive image retrieval using smoothed nearest neighbor estimates. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds.) SSPR&SPR 2010. LNCS, vol. 6218, pp. 708–717. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Zhou, X.S., Huang, T.S.: Small sample learning during multimedia retrieval using biasmap. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11–17 (2001)Google Scholar
  12. 12.
    Chen, Y., Zhou, X.S., Huang, T.S.: One-class svm for learning in image retrieval. In: Proceedings of the IEEE International Conference on Image Processing, pp. 34–37 (2001)Google Scholar
  13. 13.
    Tao, D., Tang, X., Li, X.: Which components are important for interactive image searching? IEEE Transactions on Circuits and Systems for Video Technology 18(1), 3–11 (2008)CrossRefGoogle Scholar
  14. 14.
    Arevalillo-Herráez, M., Zacarés, M., Benavent, X., de Ves, E.: A relevance feedback CBIR algorithm based on fuzzy sets. Signal Processing: Image Communication 23(7), 490–504 (2008)CrossRefGoogle Scholar
  15. 15.
    Koskela, M., Laaksonen, J., Oja, E.: Use of image subset features in image retrieval with self-organizing maps. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 508–516. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Arevalillo-Herráez, M., Ferri, F.J., Moreno-Picot, S.: Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval. Applied Soft Computing 11(2), 1782–1791 (2011)CrossRefGoogle Scholar
  17. 17.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga. Transactions on Evolutionary Computation 6(2) (April 2002)Google Scholar
  18. 18.
    Pighetti, R., Pallez, D., Precioso, F.: Hybdrid content based image retrieval combining multi-objective interactive genetic algorithm and svm. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 2849–2852 (2012)Google Scholar
  19. 19.
    Müller, H., Müller, W., Squirre, D.M.: Automated benchmarking in content based image retrieval. In: IEEE International Conference on Multimedia and Expo, pp. 321–324 (2001)Google Scholar
  20. 20.
    Laaksonen, J., Koskela, M., Oja, E.: Picsom-self-organizing image retrieval with mpeg-7 content descriptors. IEEE Transactions on Neural Networks 13(4), 841–853 (2002)CrossRefGoogle Scholar
  21. 21.
    Giacinto, G., Roli, F.: Nearest-prototype relevance feedback for content based image retrieval. In: ICPR 2004: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 989–992. IEEE Computer Society, Washington, DC (2004)CrossRefGoogle Scholar
  22. 22.
    Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Salvador Moreno-Picot
    • 1
  • Francesc J. Ferri
    • 1
  • Miguel Arevalillo-Herráez
    • 1
  1. 1.Department of Computer ScienceUniversity of ValenciaBurjasotSpain

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