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https://doi.org/10.1016/j.sigpro.2012.06.014
| Title: | Learning saliency-based visual attention: A review | Authors: | Zhao, Q. Koch, C. |
Keywords: | Central fixation bias Feature representation Machine learning Public eye tracking datasets Visual attention |
Issue Date: | Jun-2013 | Citation: | Zhao, Q., Koch, C. (2013-06). Learning saliency-based visual attention: A review. Signal Processing 93 (6) : 1401-1407. ScholarBank@NUS Repository. https://doi.org/10.1016/j.sigpro.2012.06.014 | 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. © 2012 Elsevier B.V. | Source Title: | Signal Processing | URI: | http://scholarbank.nus.edu.sg/handle/10635/56478 | ISSN: | 01651684 | DOI: | 10.1016/j.sigpro.2012.06.014 |
| Appears in Collections: | Staff Publications |
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