• DocumentCode
    2307713
  • Title

    Definition of descriptors for semantic image interpretation

  • Author

    Ivasic-Kos, Marina ; Poscic, Patrizia ; Pavlic, Mile

  • Author_Institution
    Dept. for Comput. Sci., Univ. in Rijeka, Rijeka, Croatia
  • fYear
    2010
  • fDate
    5-6 July 2010
  • Firstpage
    214
  • Lastpage
    219
  • Abstract
    A lot of effort has been put into researching image interpretation, but there is still no universally accepted approach to map low-level feature into high level image semantic interpretation. In this paper, a method for continuous low-level features vector quantization is presented so as to define appropriate values for descriptive variables. The similarity among different concepts of the domain is examined and compared by using the measure of similarity which is based on the probabilistic model and the measure of distance. Also, an abstract image description vector suitable for image analysis is given.
  • Keywords
    content-based retrieval; feature extraction; image retrieval; probability; vector quantisation; abstract image description vector; content based image retrieval; descriptive variables; high level semantic image interpretation; image analysis; low-level feature vector quantization; probabilistic model; image classification; image representations; quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Visual Information Processing (EUVIP), 2010 2nd European Workshop on
  • Conference_Location
    Paris
  • Print_ISBN
    978-1-4244-7288-8
  • Type

    conf

  • DOI
    10.1109/EUVIP.2010.5699133
  • Filename
    5699133