IEEE Trans Image Process. 2017 Aug;26(8):3775-3788. doi: 10.1109/TIP.2017.2704431. Epub 2017 May 16.
This paper aims at bridging the two important trends in efficient graph cuts in the literature, the one is to decompose a graph into several smaller subgraphs to take the advantage of parallel computation, the other is to reuse the solution of the max-flow problem on a residual graph to boost the efficiency on another similar graph. Our proposed parallel dynamic graph cuts algorithm takes the advantages of both, and is extremely efficient for certain dynamically changing MRF models in computer vision. The performance of our proposed algorithm is validated on two typical dynamic graph cuts problems: the foreground-background segmentation in video, where similar graph cuts problems need to be solved in sequential and GrabCut, where graph cuts are used iteratively.
本文旨在结合文献中高效图割的两个重要趋势，一个是将图分解为几个较小的子图，以利用并行计算的优势，另一个是在残差图上重新使用最大流问题的解，以提高另一个类似图的效率。我们提出的并行动态图割算法同时利用了这两个趋势，对于计算机视觉中某些动态变化的马尔可夫随机场模型非常有效。我们提出的算法的性能在两个典型的动态图割问题上得到了验证：视频中的前景-背景分割，其中需要顺序解决类似的图割问题，以及 GrabCut，其中图割被迭代使用。
