Content Based Image Retrieval Using Adaptive Inverse Pyramid Representation

  • Mariofanna Milanova
  • Roumen Kountchev
  • Stuart Rubin
  • Vladimir Todorov
  • Roumiana Kountcheva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5618)


This paper presents a new approach for content-based image retrieval using cognitive representation with pyramidal decomposition. This approach corresponds to the hypothesis of the human way for object recognition based on consecutive approximations with increased resolution for the selected regions of interest. The method is based on object model creation with Inverse Difference Pyramid controlled by neural network. The method’s basic advantages are the high flexibility and the ability to create general models for various views and scaling with relatively low computational complexity. The method is suitable for great number of applications – medicine, digital libraries, electronic galleries, geographic information systems, documents archiving, digital communication systems, etc.


content- based image retrieval multi-layer representation IDP decomposition 


  1. 1.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys 40(2), article 5, 60 (2008)CrossRefGoogle Scholar
  2. 2.
    Hubel, D.: Eye, Brain and Vision Scientific American Library, vol. 22. W. Freeman, New York (1989)Google Scholar
  3. 3.
    Mancas, M., Gosselin, B., Macq, B.: Perceptual Image Representation. EURaSIP Journal on Image and Visual Processing, article ID 98181 (2007)Google Scholar
  4. 4.
    Schyns, P., Oliva, A.: From blobs to boundary edges: evidence for time and spatial scale dependent scene recognition. Psychol. Sci. 5, 195–200 (1994)CrossRefGoogle Scholar
  5. 5.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Computer Vision 42, 145–175 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Carrasco, M., McElree, B.: Covert attention accelerates the rate of visual information processing. PNAS 98, 5363–5367 (2001)CrossRefGoogle Scholar
  7. 7.
    Kountchev, R., Milanova, M., Ford, C., Kountcheva, R.: Multi-layer Image Transmission with Inverse Pyramidal Decomposition. In: Halgamuge, S., Wang, L. (eds.) Computational Intelligence for Modelling and Predictions, ch.13, vol. 2, pp. 179–196. Springer, Heidelberg (2005)Google Scholar
  8. 8.
    Derrode, S., Ghorbel, F.: Robust and efficient Fourier-Mellin transform approximations for gray-level image reconstruction and complete invariant description. Computer vision and image understanding 83(1), 57–78 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Kountchev, R., Kountcheva, R.: Image Representation with Reduced Spectrum Pyramid. In: Tsihrintzis, G., Virvou, M., Howlett, R., Jain, L. (eds.) New Directions in Intelligent Interactive Multimedia, pp. 275–284. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mariofanna Milanova
    • 1
  • Roumen Kountchev
    • 2
  • Stuart Rubin
    • 3
  • Vladimir Todorov
    • 4
  • Roumiana Kountcheva
    • 4
  1. 1.Computer Science DepartmenttUALRUSA
  2. 2.Department of Radio CommunicationsTechnical University of SofiaBulgaria
  3. 3.SSC San DiegoUSA
  4. 4.T&K EngineeringBulgaria

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