OPT OpenIR  > 动态光学成像研究室
Visual Saliency Detection Based on Region Contrast and Guided Filter
Liu, Liqiang1,2; Cao, Jianzhong1; Niu, Yuefeng1; Guo, Huinan1; Liu, LQ (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China.
2017
会议名称2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)
会议录名称2017 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND APPLICATIONS (ICCIA)
页码327-330
会议日期2017-09-08
会议地点N China Univ Technol, Beijing, PEOPLES R CHINA
出版地NEW YORK
出版者IEEE
产权排序1
摘要

The main challenge of previous saliency detection method is the low quality of obtained saliency map which missed the edge and texture information easily. So it cannot reflect the integrated image salient information. Considering this problem, we propose a novel saliency measure method which combine region contrast and fast guided filter. This method utilizes region contrast method to obtain initial saliency maps. Then we optimize the saliency maps by using the fast guided filter. Extensive experimental results on natural image show the effectiveness of the proposed method. One aspect, the obtained final saliency maps have obvious advantages in dealing with the texture and weakening the inconsequential region. Another aspect, evaluation on the two databases validates that our method achieves superior results and outperforms compared previous approach in both precision and recall.

关键词Saliency Guided Filter Region Contrast Edge And Texture
学科领域Computer Science, Artificial Intelligence
作者部门动态光学成像研究室
收录类别EI ; ISTP
ISBN号978-1-5386-2030-4
语种英语
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/29962
专题动态光学成像研究室
通讯作者Liu, LQ (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China.
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714
Liu, Liqiang,Cao, Jianzhong,Niu, Yuefeng,et al. Visual Saliency Detection Based on Region Contrast and Guided Filter[C]. NEW YORK:IEEE,2017:327-330.
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