基于多层图和紧凑性的显著性检测算法  

Saliency Detection Algorithm Based on Multi-layer Graph and Compactness

作　　者：常青[1] 邵臣 CHANG Qing;SHAO Chen(School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)

机构地区：[1]华东理工大学信息科学与工程学院,上海200237

出　　处：《华东理工大学学报（自然科学版）》2018年第5期737-743,共7页Journal of East China University of Science and Technology

摘　　要：针对传统的自底向上的显著性检测模型突出背景、前景区域不均匀以及显著目标位于图像边缘致使检测效果差等问题,提出了一种基于多层图和紧凑性的显著性检测模型。首先,将图像过分割为超像素,在超像素基础上结合图像块层和聚类层构建多层图模型,能够有效检测不同尺度的图像并获得均匀的显著区域。然后,基于紧凑性假设建立紧凑性模型,并采用元胞自动机优化。根据超像素的紧凑性筛选出可靠的前景种子点和背景种子点,基于多层图模型利用流行排序算法分别计算基于前景种子点和背景种子点的排序分数,从目标和背景的角度结合两种排序分数得到显著图。最后,对显著图进行滤波获得光滑的前景和背景区域,得到最终显著图。在常用的数据集MSRA-1000和ECSSD上与9种流行算法进行比较,实验结果表明该算法具有较高的准确率和召回率。Visual salient region detection is one of the key technologies for machine vision.There have been still many problems in salient region detection,e.g.,the quality of the detection results is not high,and the whole salient region cannot be highlighted effectively,etc.Moreover,the existing salient region detection algorithms cannot effectively handle the salient region appearing in the edge of image,since they are easily affected by complex background and the color or texture of the object itself.Aiming at the shortcoming that the saliency detection model is not suitable for complex background and low contrast environment and the detection effect of saliency target located at the edge of image is poor,this paper proposes a saliency detection model based on multi-layer graph and compactness.Firstly,the image is divided into super pixels and a multi-layer graph is constructed by combining the high-level information of image block layer and cluster layer,which can effectively detect different scale images and obtain uniform salient regions.And then,both foreground and background seed nodes are extracted according to the compactness assumption.The sorting scores based on foreground and background seed nodes,respectively,are calculated by means popular algorithms via multi-layer graph model.Furthermore,the above two sorting scores are combined to get the saliency map,which is further filtered to obtain the smooth foreground and background regions and the final saliency map.The proposed multi-layer graph introduces the image block layer and the clustering layer and combines the local and global information so that it can reduce the sensitivity of seeds,obtain the uniform saliency region,and effectively detect targets of different sizes.Moreover,by means of the compactness calculation model,a more reliable seed point can be extracted to obtain more accurate results.Experimental results on MSRA-1000 database and ECSSD database show that the proposed algorithm can obtain higher accuracy and recall rate than nine popular met

关 键 词：显著性检测 多层图 紧凑性 种子扩散 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]