Ranking Images Using Customized Fuzzy Dominant Color Descriptors

  • J. M. Soto-Hidalgo
  • J. Chamorro-Martínez
  • P. Martínez-Jiménez
  • Daniel Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8132)


In this paper we describe an approach for defining customized color descriptors for image retrieval. In particular, a customized fuzzy dominant color descriptor is proposed on the basis of a finite collection of fuzzy colors designed specifically for a certain user. Fuzzy colors modeling the semantics of a color name are defined as fuzzy subsets of colors on an ordinary color space, filling the semantic gap between the color representation in computers and the subjective human perception. The design of fuzzy colors is based on a collection of color names and corresponding crisp representatives provided by the user. The descriptor is defined as a fuzzy set over the customized fuzzy colors (i.e. a level-2 fuzzy set), taking into account the imprecise concept that is modelled, in which membership degrees represent the dominance of each color. The dominance of each fuzzy color is calculated on the basis of a fuzzy quantifier representing the notion of dominance, and a fuzzy histogram representing as a fuzzy quantity the percentage of pixels that match each fuzzy color. The obtained descriptor can be employed in a large amount of applications. We illustrate the usefulness of the descriptor by a particular application in image retrieval.


Customized Fuzzy Color Dominant color descriptor Fuzzy Quantification Image retrieval 


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  1. 1.
    Chang, S.F., Sikora, T., Puri, A.: Overview of the mpeg-7 standard. IEEE Transaction on Circuits and Systems for Video Technology 11, 688–695 (2001)CrossRefGoogle Scholar
  2. 2.
    Delgado, M., Martín-Bautista, M.J., Sánchez, D., Vila, M.A.: A probabilistic definition of a nonconvex fuzzy cardinality. Fuzzy Sets and Systems 126(2), 41–54 (2002)CrossRefGoogle Scholar
  3. 3.
    Delgado, M., Sánchez, D., Vila, M.A.: Fuzzy cardinality based evaluation of quantified sentences. International Journal of Approximate Reasoning 23, 23–66 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Gabbouj, M., Birinci, M., Kiranyaz, S.: Perceptual color descriptor based on a spatial distribution model: Proximity histograms. In: International Conference on Multimedia Computing and Systems, ICMCS 2009, pp. 144–149 (2009)Google Scholar
  5. 5.
    Huang, Z., Chan, P.P.K., Ng, W.W.Y., Yeung, D.S.: Content-based image retrieval using color moment and gabor texture feature. In: 2010 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 2, pp. 719–724 (2010)Google Scholar
  6. 6.
    Li, A., Bao, X.: Extracting image dominant color features based on region growing. In: 2010 International Conference on Web Information Systems and Mining, WISM 2012, vol. 2, pp. 120–123 (2010)Google Scholar
  7. 7.
    Islam, M.M., Zhang, D., Lu, G.: Automatic categorization of image regions using dominant color based vector quantization. In: Computing: Techniques and Applications, DICTA 2008, Digital Image, pp. 191–198 (2008)Google Scholar
  8. 8.
    Marín, N., Medina, J.M., Pons, O., Sánchez, D., Vila, M.A.: Complex object comparison in a fuzzy context. Information and Software Technology 45(7), 431–444 (2003)CrossRefGoogle Scholar
  9. 9.
    Negrel, R., Picard, D., Gosselin, P.: Web scale image retrieval using compact tensor aggregation of visual descriptors. IEEE MultiMedia (99), 1 (2013)Google Scholar
  10. 10.
    Preparata, F.P., Shamos, M.I.: Computational geometry: algorithms and applications, 2nd edn. Springer, New York (1988)Google Scholar
  11. 11.
    Soto-Hidalgo, J.M., Chamorro-Martinez, J., Sanchez, D.: A new approach for defining a fuzzy color space. In: IEEE World Congress on Computational Intelligence (WCCI 2010), pp. 292–297 (July 2010)Google Scholar
  12. 12.
    van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1582–1596 (2010)CrossRefGoogle Scholar
  13. 13.
    Wu, J., Rehg, J.M.: Centrist: A visual descriptor for scene categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(8), 1489–1501 (2011)CrossRefGoogle Scholar
  14. 14.
    Yahoo! Flickr api. a programmers place to create applications @ONLINE (2013)Google Scholar
  15. 15.
    Yamada, A., Pickering, M., Jeannin, S., Jens, L.C.: Mpeg-7: Visual part of experimentation model version 9.0. ISO/IEC JTC1/SC29/WG11/N3914 (2001)Google Scholar
  16. 16.
    Yang, N., Chang, W., Kuo, C., Li, T.: A fast mpeg-7 dominant color extraction with new similarity measure for image retrieval. Journal of Visual Communication and Image Representation 19(2), 92–105 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • J. M. Soto-Hidalgo
    • 1
  • J. Chamorro-Martínez
    • 2
  • P. Martínez-Jiménez
    • 2
  • Daniel Sánchez
    • 1
    • 3
  1. 1.Department of Computer Architecture, Electronics and Electronic TechnologyUniversity of CórdobaSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaSpain
  3. 3.European Centre for Soft ComputingAsturiasSpain

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