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Introducing context-dependent and spatially-variant viewing biases in saccadic models.pdf
Vision Research 121 (2016) 72–84
Contents lists available at ScienceDirect
Vision Research
journal homepage: /locate/visres
Introducing context-dependent and spatially-variant viewing biases in saccadic models q
Olivier Le Meur a,?,1, Antoine Coutrot b,1
a IRISA University of Rennes 1, Campus Universitaire de Beaulieu, 35042 Rennes, France b CoMPLEX, University College London, United Kingdom
article info
Article history: Received 16 September 2015 Received in revised form 18 January 2016 Accepted 20 January 2016 Available online 26 February 2016
Keywords: Viewing biases Visual scanpath Visual attention Saliency
abstract
Previous research showed the existence of systematic tendencies in viewing behavior during scene exploration. For instance, saccades are known to follow a positively skewed, long-tailed distribution, and to be more frequently initiated in the horizontal or vertical directions. In this study, we hypothesize that these viewing biases are not universal, but are modulated by the semantic visual category of the stimulus. We show that the joint distribution of saccade amplitudes and orientations signi?cantly varies from one visual category to another. These joint distributions are in addition spatially variant within the scene frame. We demonstrate that a saliency model based on this better understanding of viewing behavioral biases and blind to any visual information outperforms well-established saliency models. We also propose a saccadic model that takes into account classical low-level features and spatially-variant and context-dependent viewing biases. This model outperforms state-of-the-art saliency models, and provides scanpaths in close agreement with human behavior. The better description of viewing biases will not only improve current models of visual attention but could also in?uence many other applications such as the design of human–computer interfaces, patient diagnosis or image/video processing applications.
ó 2016 Elsevier Ltd. All rights re
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