Dynamic Saliency Models and Human Attention: A Comparative Study on Videos

  • Nicolas Riche
  • Matei Mancas
  • Dubravko Culibrk
  • Vladimir Crnojevic
  • Bernard Gosselin
  • Thierry Dutoit
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)


Significant progress has been made in terms of computational models of bottom-up visual attention (saliency). However, efficient ways of comparing these models for still images remain an open research question. The problem is even more challenging when dealing with videos and dynamic saliency. The paper proposes a framework for dynamic-saliency model evaluation, based on a new database of diverse videos for which eye-tracking data has been collected. In addition, we present evaluation results obtained for 4 state-of-the-art dynamic-saliency models, two of which have not been verified on eye-tracking data before.


Independent Component Analysis Saliency Detection Visual Saliency Saliency Model Human Attention 
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 2013

Authors and Affiliations

  • Nicolas Riche
    • 1
    • 2
  • Matei Mancas
    • 1
    • 2
  • Dubravko Culibrk
    • 1
    • 2
  • Vladimir Crnojevic
    • 1
    • 2
  • Bernard Gosselin
    • 1
    • 2
  • Thierry Dutoit
    • 1
    • 2
  1. 1.University of MonsBelgium
  2. 2.University of Novi SadSerbia

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