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A brief survey of visual saliency detection

Research output: Contribution to journal › Article

Salient object detection models mimic the behavior of human beings and capture the most salient region/object from the images or scenes. This field has many important applications in both computer vision and pattern recognition tasks. Despite hundreds of models proposed in this field, it still has 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.
Original languageEnglish
Number of pages41
JournalMultimedia Tools and Applications
Early online date13 Apr 2020
DOIs
Publication statusEarly online - 13 Apr 2020

Documents

  • A Brief Survey of Visual Saliency Detection_pp

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Multimedia Tools and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11042-020-08849-y.

    Accepted author manuscript (Post-print), 1.29 MB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 13/04/21

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