IEEE Trans Image Process. 2015 Aug;24(8):2552-64. doi: 10.1109/TIP.2015.2425544. Epub 2015 Apr 22.
A novel saliency detection algorithm for video sequences based on the random walk with restart (RWR) is proposed in this paper. We adopt RWR to detect spatially and temporally salient regions. More specifically, we first find a temporal saliency distribution using the features of motion distinctiveness, temporal consistency, and abrupt change. Among them, the motion distinctiveness is derived by comparing the motion profiles of image patches. Then, we employ the temporal saliency distribution as a restarting distribution of the random walker. In addition, we design the transition probability matrix for the walker using the spatial features of intensity, color, and compactness. Finally, we estimate the spatiotemporal saliency distribution by finding the steady-state distribution of the walker. The proposed algorithm detects foreground salient objects faithfully, while suppressing cluttered backgrounds effectively, by incorporating the spatial transition matrix and the temporal restarting distribution systematically. Experimental results on various video sequences demonstrate that the proposed algorithm outperforms conventional saliency detection algorithms qualitatively and quantitatively.
本文提出了一种基于随机游走重启动（RWR）的视频序列显著度检测新算法。我们采用 RWR 来检测空间和时间显著区域。更具体地说，我们首先使用运动显著性、时间一致性和突变的特征来找到时间显著分布。其中，运动显著性是通过比较图像块的运动轮廓得出的。然后，我们将时间显著分布用作随机游走者的重启动分布。此外，我们使用强度、颜色和紧凑度的空间特征设计了用于游走者的转移概率矩阵。最后，我们通过找到游走者的稳态分布来估计时空显著分布。通过系统地结合空间转移矩阵和时间重启动分布，所提出的算法能够准确地检测前景显著对象，同时有效地抑制杂乱的背景。在各种视频序列上的实验结果表明，该算法在定性和定量方面都优于传统的显著度检测算法。
