• DocumentCode
    3713090
  • Title

    Automatic visual features weights obtention for Content-Based Image Retrieval Systems

  • Author

    Atoany Fierro-Radilla;Karina Toscano-Medina;Mariko Nakano-Miyatake;Hector Perez-Meana;Manuel Cedillo-Hernandez;Francisco Garcia-Ugalde

  • Author_Institution
    Escuela Superior de Ingenier?a Mec?nica y El?ctrica UC, Instituto Polit?cnico Nacional Mexico City, Mexico
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    In Content-Based Image Retrieval (CBIR) Systems it is necessary to combine more than one visual descriptor in order to improve the retrieval performance. The most common descriptors are Color-Based, Shape-Based and Texture-Based descriptors. When more than one visual descriptor is linearly combined, some adequate weight must be assigned to each visual feature. The most common manner is setting the same weight value for each visual feature. The sum of these values must be equal to one. However, this process does not guarantee the optimum performance of the CBIR system. In order to guarantee the best performance, it is necessary to do several experimentations to find the optimum weight values combination. This is time consuming process and ambiguous, due to the weights values depends on the nature of the databases. In this paper we proposed a scheme which computes automatically the best weight combination and guarantees the optimum performance of the CBIR system.
  • Keywords
    "Visualization","Image color analysis","Shape","Feature extraction","Cities and towns","Image retrieval"
  • Publisher
    ieee
  • Conference_Titel
    Electrical Engineering, Computing Science and Automatic Control (CCE), 2015 12th International Conference on
  • Type

    conf

  • DOI
    10.1109/ICEEE.2015.7357918
  • Filename
    7357918