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Symmetry 2018, 10(6), 183; https://doi.org/10.3390/sym10060183

A Synthetic Fusion Rule for Salient Region Detection under the Framework of DS-Evidence Theory

1
School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
2
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
3
School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250100, China
*
Authors to whom correspondence should be addressed.
Received: 26 April 2018 / Revised: 17 May 2018 / Accepted: 23 May 2018 / Published: 25 May 2018
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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. View Full-Text
Keywords: image processing; image analysis; object detection; saliency detection; DS-Evidence theory; saliency fusion image processing; image analysis; object detection; saliency detection; DS-Evidence theory; saliency fusion
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Ayoub, N.; Gao, Z.; Chen, B.; Jian, M. A Synthetic Fusion Rule for Salient Region Detection under the Framework of DS-Evidence Theory. Symmetry 2018, 10, 183.

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