Exploiting Information Theory for Filtering the Kadir Scale-Saliency Detector

  • Pablo Suau
  • Francisco Escolano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4478)


In this paper we propose a Bayesian filter for the Kadir Scale Saliency Detector. Such filter is addressed to deal with the main bottleneck of the Kadir detector, which is the scale space search for all pixels in the image. Given some statistical knowledge about images considered, we show that it is possible to discard some points before applying the Kadir detector by using Information Theory and Bayesian Analysis, increasing efficiency with low error. Our method is based on the intuitive idea that homogeneous (not salient) image regions at high scales probably will be also homogeneous at lower scales of scale space.


Computer Vision Salient Feature Image Category High Scale Scale Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Pablo Suau
    • 1
  • Francisco Escolano
    • 1
  1. 1.Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Ap. de correos 99, 03080, AlicanteSpain

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