基于流形正则化随机游走的图像显著性检测

Image Saliency Detection Based on Manifold Regularized Random Walk

针对吸收马尔可夫随机游走方法未能充分抑制显著图的中心背景区域和丢失位于图像边界的显著目标的问题,提出一种基于流形正则化随机游走的图像显著性检测方法。首先以超像素作为节点对输入图像构造全局图,通过吸收马尔可夫链随机游走算法计算得到初始显著图,再对初始显著图利用自适应阈值分割获得稳健前景查询节点。其次,为有效利用图像全局信息和局部信息的互补性,构建局部正则图以获得局部最优相似度矩阵。最后,将获得的局部最优相似度矩阵和前景查询节点信息应用于流形正则化框架中得到最终显著值结果。在公共数据集上进行实验验证,结果表明,运用本文算法的显著性检测在查准率和查全率等评价指标方面均有提升。

Owing to the problems of the absorbing Markov random walk method failing to fully suppress the central background area of the saliency map and losing parts of salient objects near the image boundary,an image saliency detection method based on manifold regularized random walk is proposed.First,a global graph with superpixels from the input image is constructed.An initial saliency map is obtained by using the absorbing Markov chain,and then an adaptive threshold is used to segment the initial saliency map to ,get robust foreground queries.Second,in order to make effective use of the complementarity of global information and local information,an optimal affinity matrix is obtained by constructing the local regular graph.Finally,the obtained optimal affinity matrix and foreground queries are applied in the manifold regularized framework to obtain the final saliency results. Experimental verifications are carried out on three public datasets.The results show that the precision and recall rate of saliency detection have been improved by the proposed method.