目的 鉴于随机游走过程对人类视觉注意力的良好描述能力，提出一种基于惰性随机游走的视觉显著性检测算法。方法 首先通过对背景超像素赋予较大的惰性因子，即以背景超像素作为惰性种子节点，在由图像超像素组成的无向图上演化惰性随机游走过程，获得初始显著性图；然后利用空间位置先验及颜色对比度先验信息对初始显著图进行修正；最终通过基于前景的惰性随机游走产生鲁棒的视觉显著性检测结果。结果 为验证算法有效性，在MSRA-1000数据库上进行了仿真实验，并与主流相关算法进行了定性与定量比较。本文算法的Receiver ROC（operating characteristic）曲线及F值均高于其他相关算法。结论 与传统基于随机过程的显著性检测算法相比，普通随机游走过程无法保证收敛到稳定状态，本文算法从理论上有效克服了该问题，提高了算法的适用性；其次，本文算法通过利用视觉转移的往返时间来刻画显著性差异，在生物视觉的模拟上更加合理贴切，与普通随机游走过程采用的单向转移时间相比，效果更加鲁棒。

Objective Research on biological vision indicates that when a human observes an object, visual attention moves from one region to another, according to different saliency, ending with the observer focusing on the most interesting regions. In mathematics, the transition process of visual attention is similar to random walk, a special case of Markov process, which describes a state transition process according to different probabilities, which finally falls into a balanced state. Based on the descriptive ability of the random walk process for human visual attention, this paper presents a visual saliency detection method based on lazy random walk. Compared with the traditional method, this paper has two contributions. First, compared with ordinary random walk, the proposed method can effectively guarantee convergence to a steady state. Second, the method is more reasonable and robust, using the commute time of lazy random walk for saliency detection. Method Lazy random walk is first performed in the background by assigning a large lazy factor to the seeds on an undirected graph generated by an image superpixel. Prior information is then used to correct the initial saliency result, including the spatial center cue by convex hull detection and the color contrast cue. Finally, a robust visual saliency result is detected by applying a similar random walk from the salient seeds, which is obtained from the last step. Result Both qualitative and quantitative evaluations on the MSRA-1000 database demonstrated the robustness and efficiency of the proposed method compared with other state-of-the-art methods. The experimental results show that the proposed method outperforms relative algorithms with respect to both the ROC curve and the F measure. Conclusion The lazy random walk-based saliency detection method proposed in this paper simulates human visual attention as well as achieves better and more robust detection results than those of other methods.