• DocumentCode
    3410298
  • Title

    Detecting and sketching the common

  • Author

    Bagon, Shai ; Brostovski, Ori ; Galun, Meirav ; Irani, Michal

  • Author_Institution
    Dept. of Comput. Sci. & Appl. Math., Weizmann Inst. of Sci., Rehovot, Israel
  • fYear
    2010
  • fDate
    13-18 June 2010
  • Firstpage
    33
  • Lastpage
    40
  • Abstract
    Given very few images containing a common object of interest under severe variations in appearance, we detect the common object and provide a compact visual representation of that object, depicted by a binary sketch. Our algorithm is composed of two stages: (i) Detect a mutually common (yet non-trivial) ensemble of `self-similarity descriptors´ shared by all the input images. (ii) Having found such a mutually common ensemble, `invert´ it to generate a compact sketch which best represents this ensemble. This provides a simple and compact visual representation of the common object, while eliminating the background clutter of the query images. It can be obtained from very few query images. Such clean sketches may be useful for detection, retrieval, recognition, co-segmentation, and for artistic graphical purposes.
  • Keywords
    image representation; object detection; optimisation; statistical analysis; binary sketch; compact sketch; compact visual representation; mutually common ensemble; object detection; query image; self similarity descriptor; Computer science; Heart; Image databases; Image retrieval; Image segmentation; Information retrieval; Mathematics; Object detection; Shape; Software libraries;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on
  • Conference_Location
    San Francisco, CA
  • ISSN
    1063-6919
  • Print_ISBN
    978-1-4244-6984-0
  • Type

    conf

  • DOI
    10.1109/CVPR.2010.5540233
  • Filename
    5540233