基于被包围状态和马尔可夫模型的显著性检测

针对图像显著性检测问题,提出一种利用被包围状态和马尔可夫模型进行图像显著性检测的方法。首先,利用被包围状态计算显著性物体的大致区域；其次,使用简单线性迭代聚类(SLIC)算法对原始图像进行处理,得到图像的超像素图,并基于超像素图建立图像的图模型；接着,将距离显著性物体大致区域最远的两条边界的超像素作为虚拟背景吸收节点,利用吸收马尔可夫链计算每个超像素的显著性值,检测出初始的显著图S₁；再以计算出的显著性物体大致区域中的超像素作为虚拟前景吸收节点,利用吸收马尔可夫链检测出初始的显著性图S₂；然后,融合S₁和S₂得到最终的显著图S；最后,利用引导滤波器对显著图S进行平滑处理得到更优的显著图。在两个数据库上的实验结果表明,提出的算法优于现有大多数算法。

Saliency Detection Based on Surroundedness and Markov Model

Aiming at solving the problem of saliency detection,this paper proposed a saliency detection algorithm based on surrouuundedness and markov model.Firstly,the surroundedness is used to predict the approximate region of the salient object for eye fixation.Secondly,a simple linear iterative clustering (SLIC) algorithm is used to process the ori-ginal image,and the graph model of the image is established based on the superpixels.Next,the superpixels of the two boundaries that are the furthest from the approximate region of the salient object are taken as the virtual background absorb nodes,the saliency value of each superpixel is calculated by the absorption Markov chain,and the initial saliency map S₁ is detected.Then,superpixels in the approximate region of the salient object is used as the virtual foreground absorption nodes,and the initial saliency map S₂ is detected by the absorption Markov chain.Then S₁ and S₂ are fused to get the final saliency map S.Finally,the guided filter is used to smooth the saliency maps and get a better saliency map.Experimental results based on two public datasets demonstrate that the proposed algorithm outperforms many state-of-the-art methods.