论文类型：
期刊论文
第一作者：
Ayoub, Naeem
通讯作者：
Ayoub, N; Gao, ZG (reprint author), Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China.; Gao, ZG (reprint author), Huaqiao Univ, Coll Comp Sci & Technol, Xiamen 361021, Peoples R China.
合写作者：
Gao, Zhenguo,Chen, Bingcai,Jian, Muwei
发表时间：
2018-06-01
发表刊物：
SYMMETRY-BASEL
收录刊物：
SCIE
文献类型：
J
卷号：
10
期号：
6
ISSN号：
2073-8994
关键字：
image processing; image analysis; object detection; saliency detection;
DS-Evidence theory; saliency fusion
摘要：
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.
是否译文：
否
