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Learning saliency-based visual attention: A review
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DOI：10.1016/j.sigpro.2012.06.014.png)
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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. (c) 2012 Elsevier B.V. All rights reserved.
Keyword:
Visual attention
Machine learning
Feature representation
Central fixation bias
Public eye tracking datasets
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IF:
3.6
论文数:
10.0K
被引数:
1.7W
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