Zhao, Qi and Koch, Christof (2013) Learning saliency-based visual attention: A review. Signal Processing, 93 (6). pp. 1401-1407. ISSN 0165-1684. http://resolver.caltech.edu/CaltechAUTHORS:20130509-083126905
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Humans and other primates shift their gaze to allocate processing resources to a subset of the visual input. Understanding and emulating the way that human observers free-view a natural scene has both scientific and economic impact. It has therefore attracted the attention from researchers in a wide range of science and engineering disciplines. With the ever increasing computational power, machine learning has become a popular tool to mine human data in the exploration of how people direct their gaze when inspecting a visual scene. This paper reviews recent advances in learning saliency-based visual attention and discusses several key issues in this topic.
|Additional Information:||© 2012 Elsevier B.V. Received 28 January 2012; Received in revised form; 5 June 2012; Accepted 9 June 2012; Available online 27 June 2012.|
|Group:||Koch Laboratory, KLAB|
|Subject Keywords:||Visual attention; Machine learning; Feature representation; Central fixation bias; Public eye tracking datasets|
|Official Citation:||Qi Zhao, Christof Koch, Learning saliency-based visual attention: A review, Signal Processing, Volume 93, Issue 6, June 2013, Pages 1401-1407, ISSN 0165-1684, 10.1016/j.sigpro.2012.06.014.|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Tony Diaz|
|Deposited On:||09 May 2013 22:11|
|Last Modified:||08 Oct 2013 21:49|
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