Learning to predict where humans look

For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene. Where eye tracking devices are not a viable option, models of saliency can be used to predict fixation locations. Most saliency approaches are based on bottom-up com...

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Bibliographic Details
Published in2009 IEEE 12th International Conference on Computer Vision pp. 2106 - 2113
Main Authors Judd, Tilke, Ehinger, Krista, Durand, Fredo, Torralba, Antonio
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2009
Subjects
Application software
Biological system modeling
Biology computing
Computer graphics
Context modeling
Human computer interaction
Image databases
Layout
Predictive models
Spatial databases
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Summary:For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene. Where eye tracking devices are not a viable option, models of saliency can be used to predict fixation locations. Most saliency approaches are based on bottom-up computation that does not consider top-down image semantics and often does not match actual eye movements. To address this problem, we collected eye tracking data of 15 viewers on 1003 images and use this database as training and testing examples to learn a model of saliency based on low, middle and high-level image features. This large database of eye tracking data is publicly available with this paper.
ISBN:9781424444205
1424444209
ISSN:1550-5499
2380-7504
DOI:10.1109/ICCV.2009.5459462