Saliency detection aims to automatically highlight the most important area in an image. Traditional saliency detection methods based on absorbing Markov chain only take into account boundary nodes and often lead to incorrect saliency detection when the boundaries have salient objects. In order to address this limitation and enhance saliency detection performance, this paper proposes a novel task-independent saliency detection method based on the bidirectional absorbing Markov chains that jointly exploits not only the boundary information but also the foreground prior and background prior cues. More specifically, the input image is first segmented into number of superpixels, and the four boundary nodes (duplicated as virtual nodes) are selected. Subsequently, the absorption time upon transition node’s random walk to the absorbing state is calculated to obtain the foreground possibility. Simultaneously, foreground prior (as the virtual absorbing nodes) is used to calculate the absorption time and get the background possibility. In addition, the two aforementioned results are fused to form a combined saliency map which is further optimized by using a cost function. Finally, the superpixel-level saliency results are optimized by a regularized random walks ranking model at multi-scale. The comparative experimental results on four benchmark datasets reveal superior performance of our proposed method over state-of-the-art methods reported in the literature. The experiments show that the proposed method is efficient and can be applicable to the bottom-up image saliency detection and other visual processing tasks.

显着性检测旨在自动突出显示图像中最重要的区域。基于吸收马尔可夫链的传统显着性检测方法仅考虑边界节点，并且当边界具有显着对象时通常会导致不正确的显着性检测。为了解决此限制并提高显着性检测性能，本文提出了一种基于双向吸收马尔可夫链的，与任务无关的显着性检测方法，该方法不仅联合利用边界信息，而且还利用前景先验和背景先验线索。更具体地，首先将输入图像分割成超像素的数量，并且选择四个边界节点（复制为虚拟节点）。后来，计算过渡节点随机行走到吸收状态时的吸收时间，以获得前景可能性。同时，使用前景先验（作为虚拟吸收节点）来计算吸收时间并获得背景可能性。另外，将上述两个结果融合在一起，形成一个组合显着图，并通过使用成本函数对其进行了优化。最后，通过多尺度正则化随机游走排序模型优化超像素级显着性结果。在四个基准数据集上的对比实验结果表明，我们提出的方法优于文献中报道的最新方法。