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Saliency in Spectral Images

  • Steven Le Moan
  • Alamin Mansouri
  • Jon Hardeberg
  • Yvon Voisin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

Even though the study of saliency for color images has been thoroughly investigated in the past, very little attention has been given to datasets that cannot be displayed on traditional computer screens such as spectral images. Nevertheless, more than a means to predict human gaze, the study of saliency primarily allows for measuring informative content. Thus, we propose a novel approach for the computation of saliency maps for spectral images. Based on the Itti model, it involves the extraction of both spatial and spectral features, suitable for high dimensionality images. As an application, we present a comparison framework to evaluate how dimensionality reduction techniques convey information from the initial image. Results on two datasets prove the efficiency and the relevance of the proposed approach.

Keywords

Visual Attention Spectral Image Hyperspectral Image Informative Content Dimensionality Reduction Technique 
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 2011

Authors and Affiliations

  • Steven Le Moan
    • 1
    • 2
  • Alamin Mansouri
    • 1
  • Jon Hardeberg
    • 2
  • Yvon Voisin
    • 1
  1. 1.Laboratoire Le2iAuxerre CédexFrance
  2. 2.ColorlabGjøvik University CollegeGjøvikNorway

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