基于最大化子模和RRWM的视频协同分割

成对视频共同运动模式的协同分割指的是同时检测出两个相关视频中共有的行为模式，是计算机视觉研究的一个热点.本文提出了一种新的成对视频协同分割方法.首先，利用稠密轨迹方法对视频运动部分进行检测，并对运动轨迹进行特征表示；然后，引入子模优化方法对单视频内的运动轨迹进行聚类分析；接着采用基于重加权随机游走的图匹配方法对成对视频运动轨迹进行匹配，该方法对出格点、变形和噪声都具有很强的鲁棒性；同时根据图匹配结果实现运动轨迹的共显著性度量；最后，将所有轨迹分类成共同运动轨迹和异常运动轨迹的问题转化为基于图割的马尔科夫随机场的二值化标签问题.通过典型运动视频数据集的比较实验，其结果验证了本文方法的有效性.

A Video Co-segmentation Algorithm by Means of Maximizing Submodular Function and RRWM

Co-segmentation of common motion pattern in a pair of videos aims to detect and segment common motion pattern of the two videos simultaneously, which has become a new hotspot in computer vision. We propose a novel method to address the problem. Firstly, we detect the movement part of the video using dense trajectories and represent the movement trajectory characteristic. Then we introduce submodular function is to group these dense trajectories in the single video. Furthermore, reweighted random walks for graph matching (RRWM) is used to match the obtained trajectory clusters in different videos, which is the robust to noise, outlier, and deformation. And the trajectory co-saliency is measured according to the matching results of RRWM. Finally, classify the trajectories into common motion trajectories and outlier motion trajectories; we formulate the problem as a binary labeling of a Markov random field (MRF) based on graph cut. Experimental results on the benchmark datasets show the effectiveness of the proposed approach.