Dynamic saliency models and human attention: a comparative study on videos

  • Authors:

  • Nicolas Riche;Matei Mancas;Dubravko Culibrk;Vladimir Crnojevic;Bernard Gosselin;Thierry Dutoit

  • Affiliations:

  • University of Mons, Belgium,University of Novi Sad, Serbia;University of Mons, Belgium,University of Novi Sad, Serbia;University of Mons, Belgium,University of Novi Sad, Serbia;University of Mons, Belgium,University of Novi Sad, Serbia;University of Mons, Belgium,University of Novi Sad, Serbia;University of Mons, Belgium,University of Novi Sad, Serbia

  • Venue:
  • ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.