Learning saliency-based visual attention: A review

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.

► An introduction of saliency detection and the role machine learning can play in this task.
► A review of learning saliency-based visual attention, particularly features and learning.
► A discussion of central fixation bias and approaches to compensate the bias.
► An introduction of recent public eye tracking database for both static and dynamic scenes.