Resource Allocation for Personalized Video Summarization
Resource Allocation for Personalized Video Summarization
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DOI：
10.1109/tmm.2013.2291967
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发表时间：
2014-02
影响因子：
7.3
通讯作者：
Fan Chen;C. Vleeschouwer;A. Cavallaro
中科院分区：
文献类型：
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作者：
Fan Chen;C. Vleeschouwer;A. Cavallaro
我们提出了一个混合的个性化摘要框架，结合自适应快进和内容截断，以产生舒适和紧凑的视频摘要。我们制定视频摘要作为一个离散的优化问题，其中最佳的摘要是通过采用拉格朗日松弛和凸壳近似来解决资源分配问题。为了权衡回放速度和感知舒适度，我们考虑与场景的静止内容相关联的信息，这对于评估视频的相关性至关重要，以及与场景活动相关联的信息，这与视觉舒适度更相关。我们通过从离散选项中选择播放速度来执行剪辑级快进，其中自然包括内容截断作为无限播放速度的特殊情况。我们展示了两个用例，即广播足球视频和监控视频的摘要提出的摘要框架。客观和主观的实验证明了所提出的方法的相关性和效率。
We propose a hybrid personalized summarization framework that combines adaptive fast-forwarding and content truncation to generate comfortable and compact video summaries. We formulate video summarization as a discrete optimization problem, where the optimal summary is determined by adopting Lagrangian relaxation and convex-hull approximation to solve a resource allocation problem. To trade-off playback speed and perceptual comfort we consider information associated to the still content of the scene, which is essential to evaluate the relevance of a video, and information associated to the scene activity, which is more relevant for visual comfort. We perform clip-level fast-forwarding by selecting the playback speeds from discrete options, which naturally include content truncation as special case with infinite playback speed. We demonstrate the proposed summarization framework in two use cases, namely summarization of broadcasted soccer videos and surveillance videos. Objective and subjective experiments are performed to demonstrate the relevance and efficiency of the proposed method.