Salient Object Detection: A Survey
It addresses the need for a deep understanding of salient object detection for researchers and practitioners, but it is incremental as it synthesizes existing work rather than introducing new methods.
This survey provides a comprehensive review of salient object detection in computer vision, covering 228 publications to analyze achievements, issues, and open problems in the field.
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.