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Review Article | Open Access

Salient object detection: A survey

Ali Borji1, Ming-Ming Cheng2( ), Qibin Hou2, Huaizu Jiang3, Jia Li4
MarkableAI, New York, USA.
TKLNDST, College of Computer Science, Nankai University, Tianjin, China.
University of Massachusetts Amherst, Amherst, MA, USA.
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China.
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Abstract

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 understandingof 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, wesurvey 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.

Keywords

salient object detectionsaliencyvisual attentionregions of interest

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Computational Visual Media
Volume 5 Issue 2,
June 2019
Pages 117-150
DOI: 10.1007/s41095-019-0149-9
Cite this article:
Borji A, Cheng M-M, Hou Q, et al. Salient object detection: A survey. Computational Visual Media, 2019, 5(2): 117-150. https://doi.org/10.1007/s41095-019-0149-9

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Revised: 22 May 2019
Accepted: 27 May 2019
Published: 21 June 2019
© The author(s) 2019

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