Fuzzy Rule-Based Classifier for Content-Based Image Retrieval

  • Tatiana Jaworska
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 183)


At present a great deal of research is being done in different aspects of Content-Based Image Retrieval System (CBIR). Thus, it is necessary to develop appropriate information systems to efficiently manage datasets. Image classification is one of the most important services in image retrieval that must support these systems. The primary issue we have addressed is: how can the fuzzy set theory be used to handle crisp data for images. We propose how to introduce fuzzy rule-based classification for image objects. To achieve this goal we have constructed fuzzy rule-based classifiers, taking into account crisp data. In this chapter we present the results of the use of this fuzzy rule-based system in our CBIR.


Fuzzy Rule Zernike Moment Graphical Object CBIR System Minor Axis Length 
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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  1. 1.
    Deb, S. (ed.): Multimedia Systems and Content-Based Image Retrieval, ch. VII and XI. IDEA Group Publishing, Melbourne (2004)Google Scholar
  2. 2.
    Ali, J.M.: Content-Based Image Classification and Retrieval: A Rule-Based System Using Rough Sets Framework. In: Ma, Z. (ed.) Artificial Intelligence for Maximizing Content Based Image Retrieval, New York, ch. IV, pp. 68–82 (2009)Google Scholar
  3. 3.
    Niblack, W., Flickner, M., et al.: The QBIC Project: Querying Images by Content Using Colour, Texture and Shape. In: SPIE 1908, pp. 173–187 (1993)Google Scholar
  4. 4.
    Ogle, V., Stonebraker, M.: CHABOT: Retrieval from a Relational Database of Images. IEEE Computer 28(9), 40–48 (1995)CrossRefGoogle Scholar
  5. 5.
    Pons, O., Vila, M.A., Kacprzyk, J.: Knowledge management in fuzzy databases. STUDFUZZ, vol. 39. Physica–Verlag, New York (2000)zbMATHGoogle Scholar
  6. 6.
    Lee, J., Kuo, J.-Y., Xue, N.-L.: A note on current approaches to extending fuzzy logic to object oriented modeling. International Journal of Intelligent Systems 16(7), 807–820 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Berzal, F., Cubero, J.C., Kacprzyk, J., Marin, N., Vila, M.A., Zadrożny, S.: A General Framework for Computing with Words in Object-Oriented Programming. In: Bouchon-Meunier, B. (ed.) International Journal of Uncertainty. Fuzziness and Knowledge-Based Systems, vol. 15(suppl.), pp. 111–131. World Scientific Publishing Company, Singapore (2007)Google Scholar
  8. 8.
    Ma, Z.M., Zhang, W.J., Ma, W.Y.: Extending object-oriented databases for fuzzy information modeling. Information Systems 29, 421–435 (2004)CrossRefGoogle Scholar
  9. 9.
    Cubero, J.C., Marin, N., Medina, J.M., Pons, O., Vila, M.A.: Fuzzy Object Management in an Object-Relational Framework. In: Proceedings of the 10th International Conference IPMU, Perugia, Italy, pp. 1775–1782 (2004)Google Scholar
  10. 10.
    Candan, K.S., Li, W.-S.: On Similarity Measures for Multimedia Database Applications. Knowledge and Information Systems (3), 30–51 (2001)Google Scholar
  11. 11.
    Jaworska, T.: Object extraction as a basic process for content-based image retrieval (CBIR) system. Opto-Electronics Review, Association of Polish Electrical Engineers (SEP) 15(4), 184–195 (2007)MathSciNetGoogle Scholar
  12. 12.
    Jaworska, T.: Database as a Crucial Element for CBIR Systems. In: Proceedings of the 2nd International Symposium on Test Automation and Instrumentation, vol. 4, pp. 1983–1986. World Publishing Corporation, Beijing (2008)Google Scholar
  13. 13.
    Chang, C.C.: Spatial match retrieval of symbolic pictures. J. Informat. Sci. Eng. 7, 405–422 (1991)Google Scholar
  14. 14.
    Chang, C.C., Wu, T.C.: An exact match retrieval scheme based upon principal component analysis. Pattern Recognition Letters 16, 465–470 (1995)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Guru, D.S., Punitha, P.: An invariant scheme for exact match retrieval of symbolic images based upon principal component analysis. Pattern Recogn. Lett. 25, 73–86 (2004)CrossRefGoogle Scholar
  16. 16.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Ishibuchi, H., Yamamoto, T.: Rule weight specification in fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems 13(4), 428–435 (2005)CrossRefGoogle Scholar
  18. 18.
    Mozaki, K., Ishibuchi, H., Tanaka, H.: Adaptive fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems 13(4), 238–250 (1996)Google Scholar
  19. 19.
    Ishibuchi, H., Nojima, Y.: Toward Quantitative Definition of Explanation Ability of fuzzy rule-based classifiers. In: IEEE International Conference on Fuzzy Systems, Taipai, Taiwan, June 27-39, pp. 549–556 (2011)Google Scholar
  20. 20.
    Teague, M.R.: Image analysis via the general theory of moments. In: JOSA, 8th edn., vol. 70, pp. 920–930 (1980)Google Scholar
  21. 21.
    Jaworska, T.: A Search-Engine Concept Based on Multi-feature Vectors and Spatial Relationship. In: Christiansen, H., De Tré, G., Yazici, A., Zadrozny, S., Andreasen, T., Larsen, H.L. (eds.) FQAS 2011. LNCS(LNAI), vol. 7022, pp. 137–148. Springer, Heidelberg (2011)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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