Keyframe-based video summarization using Delaunay clustering
Keyframe-based video summarization using Delaunay clustering
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DOI：
10.1007/s00799-005-0129-9
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发表时间：
2006-04
影响因子：
1.5
通讯作者：
P. Mundur;Yong Rao;Y. Yesha
中科院分区：
文献类型：
--
作者：
P. Mundur;Yong Rao;Y. Yesha
技术的最新进展使广大民众能够获得大量的多媒体信息。一个有效的方法来处理这个新的发展是开发浏览工具，提取多媒体数据作为面向信息的摘要。这种方法不仅适合无线和移动的等资源贫乏的环境，而且还增强了数字图书馆和存储库等应用程序在有线端的浏览。自动摘要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件，以便以后完整地观看。在本文中，我们提出了一种技术，我们可以自动收集的视频中的感兴趣的帧摘要的目的。我们提出的技术是基于使用Delaunay三角形聚类视频中的帧。我们将帧内容表示为多维点数据，并使用Delaunay三角测量对其进行聚类。我们提出了一种新的视频摘要技术，通过使用Delaunay聚类，生成质量好的摘要，与其他方案相比，帧少，冗余少。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，非常适合批处理。我们证明了这些和其他可取的性能，所提出的算法通过测试它从开放视频项目的视频集合。我们提供了一个有意义的结果之间的比较建议的摘要技术与开放视频故事板和K-均值聚类。我们评估的结果，衡量所提出的技术的内容代表性价值的指标。
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.