A brief survey of visual saliency detection

• Published in: Multimedia tools and applications Vol. 79; no. 45-46; pp. 34605 - 34645
• Main Authors: Ullah, Inam, Jian, Muwei, Hussain, Sumaira, Guo, Jie, Yu, Hui, Wang, Xing, Yin, Yilong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2020; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Heuristic methods; Machine learning; Multimedia; Multimedia Information Systems; Neural networks; Object recognition; Pattern recognition; Salience; Semantics; Special Purpose and Application-Based Systems; Saliency model; Salient object; Visual cues; Saliency detection
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-08849-y

Salient object detection models mimic the behavior of human beings and capture the most salient region/object from the images or scenes, this field contains many important applications in both computer vision and pattern recognition tasks. Despite hundreds of models that have been proposed in this field, but still, it requires a large room for research. This paper demonstrates a detailed overview of the recent progress of saliency detection models in terms of heuristic-based techniques and deep learning-based techniques. we have discussed and reviewed its co-related fields, such as Eye-fixation-prediction, RGBD salient-object-detection, co-saliency object detection, and video-saliency-detection models. We have reviewed the key issues of the current saliency models and discussed future trends and recommendations. The broadly utilized datasets and assessment strategies are additionally investigated in this paper.