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Unsupervised Video Matting via Sparse and Low-Rank Representation.
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2019-01-25 , DOI: 10.1109/tpami.2019.2895331
Dongqing Zou , Xiaowu Chen , Guangying Cao , Xiaogang Wang

A novel method, unsupervised video matting via sparse and low-rank representation, is proposed which can achieve high quality in a variety of challenging examples featuring illumination changes, feature ambiguity, topology changes, transparency variation, dis-occlusion, fast motion and motion blur. Some previous matting methods introduced a nonlocal prior to search samples for estimating the alpha matte, which have achieved impressive results on some data. However, on one hand, searching inadequate or excessive samples may miss good samples or introduce noise; on the other hand, it is difficult to construct consistent nonlocal structures for pixels with similar features, yielding video mattes with spatial and temporal inconsistency. In this paper, we proposed a novel video matting method to achieve spatially and temporally consistent matting result. Toward this end, a sparse and low-rank representation model is introduced to pursue consistent nonlocal structures for pixels with similar features. The sparse representation is used to adaptively select best samples and accurately construct the nonlocal structures for all pixels, while the low-rank representation is used to globally ensure consistent nonlocal structures for pixels with similar features. The two representations are combined to generate spatially and temporally consistent video mattes. We test our method on lots of dataset including the benchmark dataset for image matting and dataset for video matting. Our method has achieved the best performance among all unsupervised matting methods in the public alpha matting evaluation dataset for images.

中文翻译:

通过稀疏和低秩表示进行无监督的视频抠像。

提出了一种新方法,即通过稀疏和低秩表示进行无监督视频消光,该方法可以在各种具有挑战性的示例中实现高质量,这些示例包括照明变化,特征模糊,拓扑变化,透明度变化,遮挡,快速运动和运动模糊。某些以前的抠图方法在搜索样本之前引入了非局部的用于估计alpha遮罩的方法,这些方法在某些数据上取得了令人瞩目的结果。但是,一方面,搜索不足或过多的样本可能会错过好的样本或引入噪声;另一方面,很难为具有相似特征的像素构造一致的非局部结构,从而产生具有空间和时间不一致的视频遮罩。在本文中,我们提出了一种新颖的视频消光方法,以实现时空一致的消光效果。为此,引入了稀疏和低秩表示模型,以追求具有相似特征的像素的一致非局部结构。稀疏表示用于自适应选择最佳样本并为所有像素准确构造非局部结构,而低秩表示用于全局确保具有相似特征的像素的一致非局部结构。两种表示被组合以产生空间和时间上一致的视频遮片。我们在很多数据集中测试了我们的方法,包括用于图像消光的基准数据集和用于视频消光的数据集。
更新日期:2019-01-25
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