learning to model task-oriented attention

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ID: 128782
2016
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Ranked #41 of 77 articles by views in Organic Chemistry Frontiers

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Abstract
For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene with a particular task. Models of saliency can be used to predict fixation locations, but a large body of previous saliency models focused on free-viewing task. They are based on bottom-up computation that does not consider task-oriented image semantics and often does not match actual eye movements. To address this problem, we collected eye tracking data of 11 subjects when they performed some particular search task in 1307 images and annotation data of 2,511 segmented objects with fine contours and 8 semantic attributes. Using this database as training and testing examples, we learn a model of saliency based on bottom-up image features and target position feature. Experimental results demonstrate the importance of the target information in the prediction of task-oriented visual attention.
Reference Key
zou2016computationallearning Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Xiaochun Zou;Xinbo Zhao;Jian Wang;Yongjia Yang
Journal Organic Chemistry Frontiers
Year 2016
DOI
10.1155/2016/2381451
URL
http://dx.doi.org/10.1155/2016/2381451
Keywords
computer applications to medicine

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