Abstract
How predictable are human eye movements during search in real world scenes? We recorded 14 observers’ eye movements as they performed a search task (person detection) in 912 outdoor scenes. Observers were highly consistent in the regions fixated during search, even when the target was absent from the scene. These eye movements were used to evaluate computational models of search guidance from three sources: Saliency, target features, and scene context. Each of these models independently outperformed a cross-image control in predicting human fixations. Models that combined sources of guidance ultimately predicted 94% of human agreement, with the scene context component providing the most explanatory power. None of the models, however, could reach the precision and fidelity of an attentional map defined by human fixations. This work puts forth a benchmark for computational models of search in real world scenes. Further improvements in modelling should capture mechanisms underlying the selectivity of observers’ fixations during search.
Acknowledgements
The authors would like to thank two anonymous reviewers and Benjamin Tatler for their helpful and insightful comments on an earlier version of this manuscript. KAE was partly funded by a Singleton graduate research fellowship and by a graduate fellowship from an Integrative Training Program in Vision grant (T32 EY013935). BH-S was funded by a National Science Foundation Graduate Research Fellowship. This work was also funded by an NSF CAREER award (0546262) and a NSF contract (0705677) to AO, as well as an NSF CAREER award to AT (0747120). Supplementary information available on the following website: http://cvcl.mit.edu/SearchModels
Notes
1The complete dataset and analysis tools will be made available at the authors’ website.
2See additional figures on authors’ website for distribution of targets and fixations across all images in the database.
3In our validation set, the best exponent for the saliency map was .025, which is within the optimal range of .01–.3 found by Torralba et al. (2006).
4See people detector code at http://pascal.inrialpes.fr/soft/olt/
5See the authors’ website for details and results from the other implementations.
6See the authors’ website for the detection curves of the other model implementations.
7See the authors’ website for a comparison of the ROC curves of the target features model and the target oracle.