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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1401-1407 |
| Number of pages | 7 |
| Journal | Signal Processing |
| Volume | 93 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 2013 |
Keywords
- Central fixation bias
- Feature representation
- Machine learning
- Public eye tracking datasets
- Visual attention