  Detecting and segmenting salient objects from natural scenes, often referred
to as salient object detection, has attracted great interest in computer
vision. While many models have been proposed and several applications have
emerged, a deep understanding of achievements and issues remains lacking. We
aim to provide a comprehensive review of recent progress in salient object
detection and situate this field among other closely related areas such as
generic scene segmentation, object proposal generation, and saliency for
fixation prediction. Covering 228 publications, we survey i) roots, key
concepts, and tasks, ii) core techniques and main modeling trends, and iii)
datasets and evaluation metrics for salient object detection. We also discuss
open problems such as evaluation metrics and dataset bias in model performance,
and suggest future research directions.
