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Decoding What People See from Where They Look: Predicting Visual Stimuli from Scanpaths

Cerf, Moran and Harel, Jonathan and Huth, Alex and Einhaeuser, Wolfgang and Koch, Christof (2009) Decoding What People See from Where They Look: Predicting Visual Stimuli from Scanpaths. In: Attention in cognitive systems : $b 5th International Workshop on Attention in Cognitive Systems, WAPCV 2008, Fira, Santorini, Greece, May 12, 2008 : revised selected papers. Lecture Notes in Artificial Intelligence . No.5395. Springer , New York, pp. 15-26. ISBN 9783642005817.

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Saliency algorithms are applied to correlate with the overt attentional shifts, corresponding to eye movements, made by observers viewing an image. In this study, we investigated if saliency maps could be used to predict which image observers were viewing given only scanpath data. The results were strong: in an experiment with 441 trials, each consisting of 2 images with scanpath data - pooled over 9 subjects - belonging to one unknown image in the set, in 304 trials (69%) the correct image was selected, a fraction significantly above chance, but much lower than the correctness rate achieved using scanpaths from individual subjects, which was 82.4%. This leads us to propose a new metric for quantifying the importance of saliency map features, based on discriminability between images, as well as a new method for comparing present saliency map efficacy metrics. This has potential application for other kinds of predictions, e.g., categories of image content, or even subject class.

Item Type:Book Section
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URLURL TypeDescription paper
Koch, Christof0000-0001-6482-8067
Additional Information:© 2009 Springer-Verlag Berlin Heidelberg. This research was funded by the Mathers Foundation, NGA and NIMH.
Group:Koch Laboratory (KLAB)
Funding AgencyGrant Number
Mathers FoundationUNSPECIFIED
National Institute of Mental Health (NIMH)UNSPECIFIED
Series Name:Lecture Notes in Artificial Intelligence
Issue or Number:5395
Record Number:CaltechAUTHORS:20100617-151441136
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:18725
Deposited By: Tony Diaz
Deposited On:09 Jul 2010 18:24
Last Modified:08 Nov 2021 23:46

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