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Local Subspace-Based Denoising for Shot Boundary Detection

机译:基于局部子空间的镜头边界去噪

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Shot boundary detection (SBD) has long been an important problem in content based video analyzing. In existing works, researchers proposed kinds of methods to analyze the continuity of video sequence for SBD. However, the conventional methods focus on analyzing adjacent frame continuity information in some common feature space. The feature space for content representing and continuity computing is seldom specialized for different parts of video content. In this paper, we demonstrate the shortage of using common feature space, and propose a denoising method that can effectively restrain the in-shot change for SBD. A local subspace specialized for every period of video content is used to develop the denoising method. The experiment results show the proposed method can remove the noise effectively and promote the performance of SBD.
机译:镜头边界检测(SBD)一直是基于内容的视频分析中的重要问题。在现有工作中,研究人员提出了多种方法来分析SBD视频序列的连续性。然而,常规方法集中于分析某些公共特征空间中的相邻帧连续性信息。内容表示和连续性计算的特征空间很少专门用于视频内容的不同部分。在本文中,我们证明了使用公共特征空间的不足,并提出了一种可以有效抑制SBD镜头内变化的去噪方法。使用专门针对视频内容每个周期的局部子空间来开发去噪方法。实验结果表明,该方法可以有效地去除噪声,提高SBD的性能。

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