矩阵的低秩稀疏表达在视频目标分割中的研究

提出了一种视频目标跟踪与分割的在线算法。该算法将每帧图像中的超级像素作为一个数据点，根据已知的目标和背景建立模板，当前帧中待分割的目标可以看成已知模板的稀疏线性表达。根据此线性表达的系数可以建立描述当前帧与模板的相似性矩阵，即表达子。由于视频图像的连续性，表达子具有低秩和稀疏的特征。因此通过求解矩阵的低秩稀疏的优化问题可以得到当前帧中所有数据点属于目标的概率分布。为了获得基于像素级的分割结果，通过能量最小框架，并利用图分割的方法最终实现目标的分割。实验结果表明该算法具有良好的分割效果。

Video Object Segmentation Via Low-Rank Sparse Representation

We present a novel on-line algorithm for target segmentation and tracking in video. Superpixels, which are abstracted in every frame, are treated as data points in this paper. The object in current frame is represented as sparse linear combination of dictionary templates, which are generated based on the segmentation result in the previous frame. Then the algorithm capitalizes on the inherent low-rank structure of representation that are learned jointly. A low-rank sparse matrix optimal solution results in the construction of the trimap. At last, a simple energy minimization solution is adopted in segmented stage, leading to a binary pixel-wise segmentation. Experiments demonstrate that our approach is effective.