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

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ID: 111025
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.
Reference Key
ayoub2018symmetrya Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Naeem Ayoub;Zhenguo Gao;Bingcai Chen;Muwei Jian;Ayoub, Naeem;Gao, Zhenguo;Chen, Bingcai;Jian, Muwei;
Journal Symmetry
Year 2018
DOI
10.3390/sym10060183
URL
https://www.mdpi.com/2073-8994/10/6/183/htm
Keywords
Image processing image analysis object detection saliency detection ds-evidence theory saliency fusion

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