Learning saliency-based visual attention: A review

Qi Zhao, Christof Koch

Research output: Contribution to journalArticlepeer-review

88 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1401-1407
Number of pages7
JournalSignal Processing
Issue number6
StatePublished - Jun 2013


  • Central fixation bias
  • Feature representation
  • Machine learning
  • Public eye tracking datasets
  • Visual attention


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