NDLI: Medium Spatial Frequencies, a Strong Predictor of Salience

Content Provider	Springer Nature Link
Author	Urban, Fabrice Follet, Brice Chamaret, Christel Meur, Olivier Baccino, Thierry
Copyright Year	2010
Abstract	The extent to which so-called low-level features are relevant to predict gaze allocation has been widely studied recently. However, the conclusions are contradictory. Edges and luminance contrasts seem to be always involved, but literature is conflicting about contribution of the different spatial scales. It appears that experiments using man-made scenes lead to the conclusion that fixation location can be efficiently discriminated using high-frequency information, whereas mid- or low frequencies are more discriminative for natural scenes. This paper focuses on the importance of spatial scale to predict visual attention. We propose a fast attentional model and study which frequency band predicts the best fixation locations during free-viewing task. An eye-tracking experiment has been conducted using different scene categories defined by their Fourier spectrums (Coast, OpenCountry, Mountain, and Street). We found that medium frequencies (0.7–1.3 cycles per degree) globally allowed the best prediction of attention, with variability among categories. Fixation locations were found to be more predictable using medium to high frequencies in man-made street scenes and low to medium frequencies in natural landscape scenes.
Starting Page	37
Ending Page	47
Page Count	11
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	3
Issue Number	1
e-ISSN	18669964
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-11-23
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Attention Saliency map Bottom up Scene category Computational modeling Eye tracking Neurosciences Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics Computation by Abstract Devices
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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