Eurofuse 2011 pp 351-362 | Cite as

Image Reduction Using Fuzzy Quantifiers

  • D. Paternain
  • C. Lopez-Molina
  • H. Bustince
  • R. Mesiar
  • G. Beliakov
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 107)


In this work we propose an image reduction algorith based on local reduction operators. We analyze the construction of weak local reduction operators by means of aggregation functions and we analyze the effect of several aggregation functions in image reduction with original and noisy images.


Original Image Reduction Operator Aggregation Function Impulsive Noise Fuzzy Measure 
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.
    Beliakov, G., Pradera, A., Calvo, T.: Aggregation Functions: A Guide for Practitioners. Studies in Fuzziness and Soft Computing, vol. 221 (2007)Google Scholar
  2. 2.
    Bustince, H., Pagola, M., Barrenechea, E.: Construction of fuzzy indices from DI-subsethood measures: Application to the global comparison of images. Information Sciences 177, 906–929 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Bustince, H., Barrenechea, E., Pagola, M., Fernandez, J.: Interval-valued fuzzy sets constructed from matrices: Application to edge detection. Fuzzy Sets and Systems 160, 1819–1840 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Bustince, H., Calvo, T., De Baets, B., Fodor, J., Mesiar, R., Montero, J., Paternain, D., Pradera, A.: A class of aggregation functions encompassing two-dimensional OWA operators. Information Sciences 180, 1977–1989 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Calvo, T., Beliakov, G.: Aggregation functions based on penalties. Fuzzy sets and Systems 161, 1420–1436 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Chaira, T., Ray, A.K.: Fuzzy measures for color image retrieval. Fuzzy Sets and Systems 150, 545–560 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Fodor, J., Roubens, M.: Fuzzy Preference Modelling and Multicriteria Decision Support. Kluwer Academic Publishers, Dordrecht (1994)zbMATHGoogle Scholar
  8. 8.
    Jurio, A., Pagola, M., Mesiar, R., Beliakov, G., Bustince, H.: Image magnification using interval information. IEEE Transactions on Image Processing (to appear)Google Scholar
  9. 9.
    Loia, V., Sessa, S.: Fuzzy relation equations for coding/decoding processes of images and videos. Information Sciences 171, 145–172 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Di Martino, F., Loia, V., Sessa, S.: A segmentation method for image compressed by fuzzy transform. Fuzzy Sets and Systems 161, 56–74 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Perfilieva, I.: Fuzzy Transforms and Their Applications to Image Compression. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds.) WILF 2005. LNCS (LNAI), vol. 3849, pp. 19–31. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Rückschlossová, T.: Aggregation operators and invariantness. PhD thesis, Slovak University of Technology, Bratislava, Slovakia (June 2003)Google Scholar
  13. 13.
    Rückschlossová, T., Rückschloss, R.: Homogeneous aggregation operators. Kybernetika (Prague) 42(3), 279–286 (2006)MathSciNetGoogle Scholar
  14. 14.
    Unser, M., Aldroubi, A., Eden, M.: Enlargement or reduction of digital images with minimum loss of information. IEEE Transactions on Image Processing 4, 247–258 (1995)CrossRefGoogle Scholar
  15. 15.
    Xiang, S., Nie, F., Zhang, C.: Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition 41, 3600–3612 (2008)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • D. Paternain
    • 1
  • C. Lopez-Molina
    • 1
  • H. Bustince
    • 1
  • R. Mesiar
    • 2
    • 3
  • G. Beliakov
    • 4
  1. 1.Departamento de Automatica y ComputacionUniversidad Publica de NavarraPamplonaSpain
  2. 2.Department of Mathematics and Descriptive GeometrySlovak University of TechnologyBratislavaSlovakia
  3. 3.Institute of Information Theory and AutomationCzech Academy of SciencesPragueCzech Republic
  4. 4.School of Information TechnologyDeakin UniversityBurwoodAustralia

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