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Multi-dimensional Scale Saliency Feature Extraction Based on Entropic Graphs

  • P. Suau
  • F. Escolano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5359)

Abstract

In this paper we present a multi-dimensional version of the Kadir and Brady scale saliency feature extractor, based on Entropic Graphs and Rényi alpha-entropy estimation. The original Kadir and Brady algorithm is conditioned by the curse of dimensionality when estimating entropy from multi-dimensional data like RGB intensity values. Our approach naturally allows to increase dimensionality, being its computation time slightly affected by the number of dimensions. Our computation time experiments, based on hyperspectral images composed of 31 bands, demonstrate that our approach can be applied to computer vision fields, i.e. hyperspectral or satellite imaging, that can not be solved by means of the original algorithm.

Keywords

Shannon Entropy Hyperspectral Image Saliency Detection Local Entropy Entropy Estimation 
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-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • P. Suau
    • 1
  • F. Escolano
    • 1
  1. 1.Robot Vision Group, Departamento de Ciencia de la Computación e IAUniversidad de AlicanteSpain

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