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. 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. 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. 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.