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Hierarchical keyframe-based video summarization using QR-decomposition and modified k-means clustering

机译:使用QR分解和改进的k均值聚类的基于关键帧的视频摘要

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摘要

We propose a novel hierarchical keyframe-based video summarization system using QR-decomposition. Specially, we attend to the challenges of defining some measures to detect the dynamicity of a shot and video and extracting appropriate keyframes that assure the purity of video summary. We derive some efficient measures to compute the dynamicity of video shots using QR-decomposition, and we utilize it in detecting the number of keyframes that must be selected from each shot. Also, we derive a theorem that illustrates an important property of QR-decomposition. We utilize it in order to summarize video shots with low redundancy. The proposed algorithm is implemented and evaluated on TRECVID 2006 benchmark platform. Compared with other algorithms, our results are among the best.
机译:我们提出了一种使用QR分解的新颖的基于分层关键帧的视频摘要系统。特别是,我们面临着定义一些措施来检测镜头和视频的动态并提取适当的关键帧以确保视频摘要纯净的挑战。我们导出了一些有效的方法来使用QR分解来计算视频镜头的动态性,并将其用于检测必须从每个镜头中选择的关键帧的数量。同样,我们推导出一个定理,该定理说明了QR分解的重要性质。我们利用它来总结低冗余的视频镜头。该算法在TRECVID 2006基准平台上实现和评估。与其他算法相比,我们的结果是最好的。

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