Journal Article10.1007/S11042-020-08849-Y
A brief survey of visual saliency detection
TL;DR: A detailed overview of the recent progress of saliency detection models in terms of heuristic- based techniques and deep learning-based techniques is demonstrated.
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Abstract: 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.
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References
Color Feature Reinforcement for Cosaliency Detection Without Single Saliency Residuals
Huang Rui,Wei Feng,Jizhou Sun +2 more
TL;DR: This letter shows that problem of single saliency residual effect in cosaliency detection can be solved by color feature reinforcement, based on a simple observation that cosalient objects usually have similar color distributions in an abundant color feature space.
Co-Saliency Detection via Co-Salient Object Discovery and Recovery
TL;DR: Experimental results on two benchmark datasets demonstrate that the proposed co-saliency model outperforms the state-of-the-art co- saliency models.
Moving object detection in aerial video based on spatiotemporal saliency
TL;DR: A novel hierarchical moving target detection method based on spatiotemporal saliency is proposed, which can get refined detection results by combining temporal and spatial saliency information.
Local Background Enclosure for RGB-D Salient Object Detection
David Feng,Nick Barnes,Shaodi You,Chris McCarthy +3 more
- 27 Jun 2016
TL;DR: Local Background Enclosure (LBE) captures the spread of angular directions which are background with respect to the candidate region and the object that it is part of and improves over state-of-the-art RGB-D saliency approaches as well as RGB methods on the RGBD1000 and NJUDS2000 datasets.
Global Contrast Based Salient Region Detection
Ming-Ming Cheng,Guo-Xin Zhang,Niloy J. Mitra,Xiaolei Huang,Shi-Min Hu +4 more
TL;DR: This paper proposes a regional contrast-based saliency extraction algorithm that evaluates global contrast differences and spatial coherence, outperforming existing methods with higher precision and recall rates, and enabling high-quality segmentation masks for image processing.
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