标题:Saliency Detection via Combining Global Shape and Local Cue Estimation
作者:Qi, Qiang; Jian, Muwei; Yin, Yilong; Dong, Junyu; Zhang, Wenyin; Yu, Hui
通讯作者:Jian, Muwei
作者机构:[Qi, Qiang; Jian, Muwei; Yin, Yilong] Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China.; [Qi, Qiang; Jian, Muw 更多
会议名称:7th International Conference on Intelligence Science and Big Data Engineering (IScIDE)
会议日期:SEP 22-23, 2017
来源:INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING, ISCIDE 2017
出版年:2017
卷:10559
页码:325-334
DOI:10.1007/978-3-319-67777-4_28
关键词:Saliency detection; QDWD; Locality-constrained linear coding; Local cue
摘要:Recently, saliency detection has become a hot issue in computer vision. In this paper, a novel framework for image saliency detection is introduced by modeling global shape and local cue estimation simultaneously. Firstly, Quaternionic Distance Based Weber Descriptor (QDWD), which was initially designed for detecting outliers in color images, is used to model the salient object shape in an image. Secondly, we detect local saliency based on the reconstruction error by using a locality-constrained linear coding algorithm. Finally, by integrating global shape with local cue, a reliable saliency map can be computed and estimated. Experimental results, based on two widely used and openly available databases, show that the proposed method can produce reliable and promising results, compared to other state-of-the-art saliency-detection algorithms.
收录类别:CPCI-S;EI
资源类型:会议论文
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