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

针对目前基于先验背景的显著度算法中,把图像的所有边界同等对待带来的误判别问题,本文提出一种基于可区分边界和加权对比度优化的显著度检测算法.为了客观评价显著度,本文首先设计了一种粗略评估显著度的指标,用来选择较好的背景图.以该指标为基础,该算法先利用Hausdorff距离对边界进行区分,再利用测地线距离变换完成可靠的背景检测;然后,构造了一种前景-背景加权的对比度来计算初始显著度;最后,使用加权的优化模型进行显著度的优化.在5个公开数据集上的实验结果表明,本文算法在保持快速、无训练等优点的同时,检测性能优于目前主流算法.

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.