• DocumentCode
    3405132
  • Title

    Saliency detection via statistical non-redundancy

  • Author

    Jain, Abhishek ; Wong, Alexander ; Fieguth, Paul

  • Author_Institution
    Dept. of Syst. Design Eng., Univ. of Waterloo, Waterloo, ON, Canada
  • fYear
    2012
  • fDate
    Sept. 30 2012-Oct. 3 2012
  • Firstpage
    1073
  • Lastpage
    1076
  • Abstract
    A novel algorithm based on statistical non-redundancy is proposed for saliency detection in natural images. By modeling site neighbourhoods as realizations of other site neighbourhoods under a Gaussian process, the saliency of any arbitrary site can be characterized by the statistical non-redundancy of its site neighbourhood with respect to other site neighbourhoods in a given image. Preliminary results using natural images show that the proposed method provides improved precision vs. recall characteristics over previous methods such as spectral residuals and spectral whitening.
  • Keywords
    Gaussian processes; object detection; Gaussian process; arbitrary site; natural images; precision improvement; recall characteristics; saliency detection; site neighbourhood modeling; spectral residuals; spectral whitening; statistical nonredundancy; Computational modeling; Computer vision; Conferences; Object recognition; Pattern recognition; Redundancy; Visualization; image modelling; object detection; saliency; statistical non-redundancy;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2012 19th IEEE International Conference on
  • Conference_Location
    Orlando, FL
  • ISSN
    1522-4880
  • Print_ISBN
    978-1-4673-2534-9
  • Electronic_ISBN
    1522-4880
  • Type

    conf

  • DOI
    10.1109/ICIP.2012.6467049
  • Filename
    6467049