• DocumentCode
    2172823
  • Title

    How to perform texture recognition from stochastic modeling in the wavelet domain

  • Author

    Atto, Abdourrahmane M. ; Berthoumieu, Yannick

  • Author_Institution
    Lab. IMS, Univ. de Bordeaux, Talence, France
  • fYear
    2011
  • fDate
    22-27 May 2011
  • Firstpage
    4320
  • Lastpage
    4323
  • Abstract
    The paper addresses content-based image retrieval from texture data bases, by using stochastic modeling in the wavelet domain. It pro poses an analysis of the key parameters involved in such a content based texture retrieval. These parameters are the wavelet order and the goodness-of-fit measure used to select the best family of distributions for modeling the subband wavelet coefficients. It is shown that taking suitable parameters into consideration makes it possible to attain high retrieval rates in content-based texture retrieval.
  • Keywords
    content-based retrieval; image recognition; image retrieval; image texture; stochastic processes; wavelet transforms; content based image retrieval; goodness-of-fit measure; stochastic modeling; subband wavelet coefficients; texture databases; texture recognition; wavelet domain; wavelet order; Computational modeling; Databases; Optical fibers; Stochastic processes; Wavelet packets; Similarity; Stochastic modeling; Texture; Wavelets;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
  • Conference_Location
    Prague
  • ISSN
    1520-6149
  • Print_ISBN
    978-1-4577-0538-0
  • Electronic_ISBN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2011.5947309
  • Filename
    5947309