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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Abstract
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.
| Item Type: | Article | |||||||||
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| 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 | |||||||||
| Record Number: | CaltechAUTHORS:20130509-083126905 | |||||||||
| Persistent URL: | http://resolver.caltech.edu/CaltechAUTHORS:20130509-083126905 | |||||||||
| 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. | |||||||||
| ID Code: | 38375 | |||||||||
| Collection: | CaltechAUTHORS | |||||||||
| Deposited By: | Tony Diaz | |||||||||
| Deposited On: | 09 May 2013 22:11 | |||||||||
| Last Modified: | 08 Oct 2013 21:49 |
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