Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4969325 | Journal of Visual Communication and Image Representation | 2017 | 23 Pages |
Abstract
Identifying visual attention plays an important role in understanding human behavior and optimizing relevant multimedia applications. In this paper, we propose a visual attention identification method based on random walks. In the proposed method, fixations recorded by the eye tracker are partitioned into clusters where each cluster presents a particular area of interest (AOI). In each cluster, we estimate the transition probabilities of the fixations based on their point-to-point adjacency in their spatial positions. We obtain the initial coefficients for the fixations according to their density. We utilizing random walks to iteratively update the coefficients until their convergency. Finally, the center of the AOI is calculated according to the convergent coefficients of the fixations. Experimental results demonstrate that our proposed method which combines the fixations' spatial and temporal relations, highlights the fixations of higher densities and eliminates the errors inside the cluster. It is more robust and accurate than traditional methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Xiu Chen, Zhenzhong Chen,