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- Title
A Synthetic Fusion Rule for Salient Region Detection under the Framework of DS-Evidence Theory.
- Authors
Ayoub, Naeem; Gao, Zhenguo; Chen, Bingcai; Jian, Muwei
- Abstract
Saliency detection is one of the most valuable research topics in computer vision. It focuses on the detection of the most significant objects/regions in images and reduces the computational time cost of getting the desired information from salient regions. Local saliency detection or common pattern discovery schemes were actively used by the researchers to overcome the saliency detection problems. In this paper, we propose a bottom-up saliency fusion method by taking into consideration the importance of the DS-Evidence (Dempster–Shafer (DS)) theory. Firstly, we calculate saliency maps from different algorithms based on the pixels-level, patches-level and region-level methods. Secondly, we fuse the pixels based on the foreground and background information under the framework of DS-Evidence theory (evidence theory allows one to combine evidence from different sources and arrive at a degree of belief that takes into account all the available evidence). The development inclination of image saliency detection through DS-Evidence theory gives us better results for saliency prediction. Experiments are conducted on the publicly available four different datasets (MSRA, ECSSD, DUT-OMRON and PASCAL-S). Our saliency detection method performs well and shows prominent results as compared to the state-of-the-art algorithms.
- Subjects
DEMPSTER-Shafer theory; IMAGE processing; IMAGE analysis; COMPUTER vision; IMAGE segmentation
- Publication
Symmetry (20738994), 2018, Vol 10, Issue 6, p183
- ISSN
2073-8994
- Publication type
Article
- DOI
10.3390/sym10060183