Video summarization via minimum sparse reconstruction
Video summarization via minimum sparse reconstruction
复制标题
通过最小稀疏重建进行视频摘要
DOI：
10.1016/j.patcog.2014.08.002
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发表时间：
2015-02-01
影响因子：
8
通讯作者：
Feng, David Dagan
中科院分区：
文献类型：
--
作者：
Mei, Shaohui;Guan, Genliang;Feng, David Dagan
视频数据的快速增长需要有效、高效的视频摘要方法，以便用户能够快速浏览和理解大量的视频内容。在本文中，我们用一种新颖的最小稀疏重建（MSR）问题制定了视频摘要任务。也就是说，可以使用尽可能少的选定关键帧来最好地重建原始视频序列。与最近提出的基于凸松弛的稀疏字典选择方法不同，我们提出的方法利用真正的稀疏约束L-0范数，而不是松弛约束L-2，L-1范数，从而直接选择关键帧作为稀疏字典，可以很好地重建所有视频帧。由于所提出的 MSR 原理的实时效率，进一步开发了在线版本。此外，提出了重建百分比（POR）准则，直观地指导用户获得适当长度的摘要。具有不同类型视频的两个基准数据集的实验结果表明，所提出的方法优于现有技术。 (C) 2014 Elsevier Ltd. 保留所有权利。
The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L-0 norm, instead of the relaxed constraint L-2,L-1 norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art. (C) 2014 Elsevier Ltd. All rights reserved.