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Learning saliency-based visual attention: A review

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
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1016/j.sigpro.2012.06.014DOIArticle
http://www.sciencedirect.com/science/article/pii/S0165168412002058PublisherArticle
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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