In this paper, we propose a novel saliency model based on double random walks with dual restarts. Two agents (also known as walkers) respectively representing the foreground and background properties simultaneously walk on a graph to explore saliency distribution. First, we propose the propagation distance measure and use it to calculate the initial distributions of the two agents instead of using geodesic distance. Second, the two agents traverse the graph starting from their own initial distribution, and then interact with each other to correct their travel routes by the restart mechanism, which enforces the agents to return to some specific nodes with a certain probability after every movement. We define the dual restarts to take into account interaction between and weighting of two agents. Extensive evaluations demonstrate that the proposed algorithm performs favorably against other state-of-the-art methods on four benchmark datasets.

在本文中，我们提出了一种基于双随机游走和双重启的显着性模型。分别代表前景和背景属性的两个代理（也称为步行者）同时在图形上行走以探索显着性分布。首先，我们提出传播距离测度，并使用它来计算两个代理的初始分布，而不是使用测地距离。其次，两个代理从它们自己的初始分布开始遍历该图，然后通过重新启动机制相互交互以更正其旅行路线，该机制强制代理在每次移动后以一定概率返回到某些特定节点。我们定义双重重启以考虑两个代理之间的交互和权重。