Saliency Revisited: Analysis of Mouse Movements versus Fixations

Hamed Rezazadegan Tavakoli, Fawad Ahmed, Ali Borji, Jorma Laaksonen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

25 Citations (Scopus)


This paper revisits visual saliency prediction by evaluating the recent advancements in this field such as crowd-sourced mouse tracking-based databases and contextual annotations. We pursue a critical and quantitative approach towards some of the new challenges including the quality of mouse tracking versus eye tracking for model training and evaluation. We extend quantitative evaluation of models in order to incorporate contextual information by proposing an evaluation methodology that allows accounting for contextual factors such as text, faces, and object attributes. The proposed contextual evaluation scheme facilitates detailed analysis of models and helps identify their pros and cons. Through several experiments, we find that (1) mouse tracking data has lower inter-participant visual congruency and higher dispersion, compared to the eye tracking data, (2) mouse tracking data does not totally agree with eye tracking in general and in terms of different contextual regions in specific, and (3) mouse tracking data leads to acceptable results in training current existing models, and (4) mouse tracking data is less reliable for model selection and evaluation. The contextual evaluation also reveals that, among the studied models, there is no single model that performs best on all the tested annotations.
Original languageEnglish
Title of host publication2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN (Electronic)978-1-5386-0457-1
Publication statusPublished - 2017
MoE publication typeA4 Conference publication
EventIEEE Conference on Computer Vision and Pattern Recognition - Honolulu, United States
Duration: 21 Jul 201626 Jul 2016
Conference number: 30

Publication series

NameIEEE Conference on Computer Vision and Pattern Recognition
ISSN (Electronic)1063-6919


ConferenceIEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR
Country/TerritoryUnited States


  • Mice
  • Measurement
  • Context modeling
  • Gaze tracking
  • Databases
  • Analytical models
  • Visualization


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