Keyframe-based video summarization using Delaunay clustering

  • Authors:

  • Padmavathi Mundur;Yong Rao;Yelena Yesha

  • Affiliations:

  • Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, 21250, Baltimore, MD, USA;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, 21250, Baltimore, MD, USA;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, 21250, Baltimore, MD, USA

  • Venue:
  • International Journal on Digital Libraries
  • Year:
  • 2006

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Abstract

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.