Fusion of Local and Global Descriptors for Content-Based Image and Video Retrieval

  • Felipe S. P. Andrade
  • Jurandy Almeida
  • Hélio Pedrini
  • Ricardo da S.Torres
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


Recently, fusion of descriptors has become a trend for improving the performance in image and video retrieval tasks. Descriptors can be global or local, depending on how they analyze visual content. Most of existing works have focused on the fusion of a single type of descriptor. Different from all of them, this paper aims to analyze the impact of combining global and local descriptors. Here, we perform a comparative study of different types of descriptors and all of their possible combinations. Extensive experiments of a rigorous experimental design show that global and local descriptors complement each other, such that, when combined, they outperform other combinations or single descriptors.


visual information retrieval image and video descriptor information fusion genetic programming performance evaluation 


  1. 1.
    Almeida, J., Leite, N.J., Torres, R.S.: Comparison of video sequences with histograms of motion patterns. In: Int. Conf. Image Proc. (ICIP), pp. 3673–3676 (2011)Google Scholar
  2. 2.
    Almeida, J., Leite, N.J., Torres, R.S.: VISON: VIdeo Summarization for ONline applications. Pattern Recognition Letters 33(4), 397–409 (2012)CrossRefGoogle Scholar
  3. 3.
    Almeida, J., Torres, R.S., Leite, N.J.: Rapid video summarization on compressed video. In: Int. Symp. Multimedia (ISM), pp. 113–120 (2010)Google Scholar
  4. 4.
    Almeida, J., Rocha, A., Torres, R.S., Goldenstein, S.: Making colors worth more than a thousand words. In: Int. Symp. Appl. Comput. (SAC), pp. 1180–1186 (2008)Google Scholar
  5. 5.
    Ferreira, C.D., Santos, J.A., Torres, R.S., Gonçalves, M.A., Rezende, R.C., Fan, W.: Relevance feedback based on genetic programming for image retrieval. Pattern Recognition Letters 32(1), 27–37 (2011)CrossRefGoogle Scholar
  6. 6.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)Google Scholar
  7. 7.
    Liu, Y., Zhang, D., Lu, G., Ma, W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40(1), 262–282 (2007)zbMATHCrossRefGoogle Scholar
  8. 8.
    Penatti, O.A.B., Valle, E., Torres, R.S.: Comparative study of global color and texture descriptors for web image retrieval. J. Visual Commun. Image Representation 23(2), 359–380 (2012)CrossRefGoogle Scholar
  9. 9.
    Salgian, A.: Combining local descriptors for 3D object recognition and categorization. In: Int. Conf. Pattern Recognition (ICPR), pp. 1–4 (2008)Google Scholar
  10. 10.
    Torres, R.S., Falcão, A.X.: Content-Based Image Retrieval: Theory and Applications. J. Theoretical and Applied Informatics 13(2), 161–185 (2006)Google Scholar
  11. 11.
    Torres, R.S., Falcão, A.X., Gonçalves, M.A., Papa, J.P., Zhang, B., Fan, W., Fox, E.A.: A genetic programming framework for content-based image retrieval. Pattern Recognition 42(2), 283–292 (2009)zbMATHCrossRefGoogle Scholar
  12. 12.
    Wu, Y.: Shape-based image retrieval using combining global and local shape features. In: Int. Congress Image and Signal Processing (CISP), pp. 1–5 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Felipe S. P. Andrade
    • 1
  • Jurandy Almeida
    • 1
  • Hélio Pedrini
    • 1
  • Ricardo da S.Torres
    • 1
  1. 1.Institute of ComputingUniversity of Campinas – UNICAMPCampinasBrazil

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