基于背景先验与中心先验的显著性目标检测

Salient object region detection based on background-bias prior and center-biasPrior

针对传统的图流行排序显著性目标检测算法存在先验信息单一,显著目标检测不完整的问题,提出一种新的基于背景先验与中心先验的显著性目标检测算法。首先将图像边界节点作为背景种子进行流行排序获得粗略的前景区域,将其再次流行排序得到初步显著图;然后利用Harris角点检测、聚类实现中心先验显著性检测,捕获中心显著信息;最后在初步显著图上融合图像中心显著性,得到最终显著图。本文对综合指标、精确率-召回率曲线、F-measure值以及平均绝对误差(mean absolute error,MAE)值进行实验评估,在公开数据集MSRA-10K和ECSSD上进行的实验结果表明:对比10种主流算法,本文算法在不同的评估指标上都具有较好的表现,且能准确地突出显著目标,提升背景抑制效果。

In order to solve the problems of single prior information and incomplete salient object detection in the traditional algorithm,a new salient object detection algorithm based on background prior and center prior is proposed.Firstly,the edge nodes of the image are used as background seeds to manifold ranking,and the rough foreground area is obtained;Then,Harris corner detection and clustering are used to detect the prior significance of the center and capture the significant information of the center;Finally,the final saliency map is obtained by fusing the center saliency on the preliminary saliency map.In this paper,the comprehensive index,precision recall curve,F-measure value and mean absolute error(MAE)value of average absolute error are evaluated experimentally.The experimental results on the open data sets MSRA-10 K and ECSSD show that compared with 10 mainstream algorithms,the algorithm in this paper has good performance in different evaluation indicators,and can accurately highlight significant targets andimprove the effect of background suppression.