Linear vs. Nonlinear Feature Combination for Saliency Computation: A Comparison with Human Vision

  • Nabil Ouerhani
  • Alexandre Bur
  • Heinz Hügli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


In the heart of the computer model of visual attention, an interest or saliency map is derived from an input image in a process that encompasses several data combination steps. While several combination strategies are possible and the choice of a method influences the final saliency substantially, there is a real need for a performance comparison for the purpose of model improvement. This paper presents contributing work in which model performances are measured by comparing saliency maps with human eye fixations. Four combination methods are compared in experiments involving the viewing of 40 images by 20 observers. Similarity is evaluated qualitatively by visual tests and quantitatively by use of a similarity score. With similarity scores lying 100% higher, non-linear combinations outperform linear methods. The comparison with human vision thus shows the superiority of non-linear over linear combination schemes and speaks for their preferred use in computer models.


Visual Attention Saliency Computation Human Visual Attention Linear Combination Scheme Local Orientation Feature 
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 2006

Authors and Affiliations

  • Nabil Ouerhani
    • 1
  • Alexandre Bur
    • 1
  • Heinz Hügli
    • 1
  1. 1.Institute of MicrotechnologyUniversity of NeuchâtelNeuchâtelSwitzerland

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