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Keyframe-based video summarization using Delaunay clustering

机译:使用Delaunay聚类的基于关键帧的视频摘要

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Recent advances in technology have made tremendous amounts of multimedia information available to the general population. An efficient way of dealing with this new development is to develop browsing tools that distill multimedia data as information oriented summaries. Such an approach will not only suit resource poor environments such as wireless and mobile, but also enhance browsing on the wired side for applications like digital libraries and repositories. Automatic summarization and indexing techniques will give users an opportunity to browse and select multimedia document of their choice for complete viewing later. In this paper, we present a technique by which we can automatically gather the frames of interest in a video for purposes of summarization. Our proposed technique is based on using Delaunay Triangulation for clustering the frames in videos. We represent the frame contents as multi-dimensional point data and use Delaunay Triangulation for clustering them. We propose a novel video summarization technique by using Delaunay clusters that generates good quality summaries with fewer frames and less redundancy when compared to other schemes. In contrast to many of the other clustering techniques, the Delaunay clustering algorithm is fully automatic with no user specified parameters and is well suited for batch processing. We demonstrate these and other desirable properties of the proposed algorithm by testing it on a collection of videos from Open Video Project. We provide a meaningful comparison between results of the proposed summarization technique with Open Video storyboard and K-means clustering. We evaluate the results in terms of metrics that measure the content representational value of the proposed technique.
机译:技术上的最新进展已为广大民众提供了大量的多媒体信息。处理这一新发展的有效方法是开发浏览工具,将多媒体数据提取为面向信息的摘要。这种方法不仅适合资源匮乏的环境(例如无线和移动环境),而且还可以增强有线方面对数字图书馆和存储库等应用程序的浏览。自动汇总和索引技术将使用户有机会浏览和选择他们所选择的多媒体文档,以便以后进行完整查看。在本文中,我们提出了一种技术,通过该技术我们可以自动收集视频中感兴趣的帧以进行汇总。我们提出的技术基于使用Delaunay三角剖分对视频中的帧进行聚类。我们将帧内容表示为多维点数据,并使用Delaunay三角剖分对其进行聚类。通过使用Delaunay聚类,我们提出了一种新颖的视频摘要技术,与其他方案相比,该技术可生成质量更好的摘要,具有较少的帧和较少的冗余。与许多其他聚类技术相比,Delaunay聚类算法是全自动的,没有用户指定的参数,非常适合于批处理。我们通过在来自Open Video Project的视频集合中对其进行测试来证明所提出算法的这些以及其他理想属性。我们在使用Open Video故事板和K-means聚类的拟议摘要技术的结果之间提供了有意义的比较。我们根据衡量所提出技术的内容代表性价值的指标来评估结果。

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