电子学报2017，Vol.45Issue(1)：147-156,10.DOI:10.3969/j.issn.0372-2112.2017.01.021
基于可区分边界和加权对比度优化的显著度检测算法
Saliency Detection Based on Discriminative Boundary and Weighted Contrast Optimization
摘要
Abstract
To address the misjudgment caused by all boundaries of an image being equally and artificially selected as background in most of state-of-the-art models using background prior,this paper proposes an algorithm called weighted contrast optimization based on discriminative background.Firstly,a metric is constructed to roughly but objectively estimate a saliency map,which is used to choose a better background map.Based on this metric,a reliable background detection model is constructed through geodesic distance transformation after discriminating each boundary via Hausdorff distance.Then,the only background weighted contrast is improved into fore-background weighted contrast.Last,the final saliency map is obtained through weighted optimization framework.Extensive experiments on five public datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods.关键词
显著度检测/背景图/可区分边界/加权对比度Key words
saliency detection/background map/discriminative boundary/weighted contrast分类
信息技术与安全科学引用本文复制引用
姜青竹,田畅,吴泽民,刘涛,张磊..基于可区分边界和加权对比度优化的显著度检测算法[J].电子学报,2017,45(1):147-156,10.基金项目
国家自然科学基金青年基金(No.61501509) （No.61501509）
