Exploiting color name space for salient object detection

被引:8
作者
Lou, Jing [1 ]
Wang, Huan [2 ]
Chen, Longtao [2 ]
Xu, Fenglei [2 ]
Xia, Qingyuan [2 ]
Zhu, Wei [2 ]
Ren, Mingwu [2 ]
机构
[1] Changzhou Vocat Inst Mechatron Technol, Sch Informat Engn, Changzhou 213164, Jiangsu, Peoples R China
[2] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Saliency; Salient object detection; Figure-ground segregation; Surroundedness; Color names; Color name space; REGION DETECTION; VISUAL-ATTENTION; IMAGE; INTEGRATION; MODEL;
D O I
10.1007/s11042-019-07970-x
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we will investigate the contribution of color names for the task of salient object detection. An input image is first converted to color name space, which is consisted of 11 probabilistic channels. By exploiting a surroundedness cue, we obtain a saliency map through a linear combination of a set of sequential attention maps. To overcome the limitation of only using the surroundedness cue, two global cues with respect to color names are invoked to guide the computation of a weighted saliency map. Finally, we integrate the above two saliency maps into a unified framework to generate the final result. In addition, an improved post-processing procedure is introduced to effectively suppress image backgrounds while uniformly highlight salient objects. Experimental results show that the proposed model produces more accurate saliency maps and performs well against twenty-one saliency models in terms of three evaluation metrics on three public data sets.
引用
收藏
页码:10873 / 10897
页数:25
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