Automatic Summarization of Rushes Video Using Bipartite Graphs

  • Authors:

  • Liang Bai;Songyang Lao;Alan F. Smeaton;Noel E. O'Connor

  • Affiliations:

  • Sch. of Information System & Management, National Univ. of Defense Technology, ChangSha, R.P. China 410073 and Centre for Digital Video Processing, Adaptive Information Cluster, Dublin City Univer ...;Sch. of Information System & Management, National Univ. of Defense Technology, ChangSha, R.P. China 410073;Centre for Digital Video Processing, Adaptive Information Cluster, Dublin City University, Glasnevin, Dublin 9, Ireland;Centre for Digital Video Processing, Adaptive Information Cluster, Dublin City University, Glasnevin, Dublin 9, Ireland

  • Venue:
  • SAMT '08 Proceedings of the 3rd International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
  • Year:
  • 2008

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Abstract

In this paper we present a new approach for automatic summarization of rushes video. Our approach is composed of three main steps. First, based on a temporal segmentation, we filter sub-shots with low information content not likely to be useful in a summary. Second, a method using maximal matching in a bipartite graph is adapted to measure similarity between the remaining shots and to minimize inter-shot redundancy by removing repetitive retake shots common in rushes content. Finally, the presence of faces and the motion in-tensity are characterised in each sub-shot. A measure of how representative the sub-shot is in the context of the overall video is then proposed. Video summaries composed of keyframe slideshows are then generated. In order to evaluate the effectiveness of this approach we re-run the evaluation carried out by the TREC, using the same dataset and evaluation metrics used in the TRECVID video summarization task in 2007 but with our own assessors. Results show that our approach leads to a significant improvement in terms of the fraction of the TRECVID summary ground truth included and is competitive with other approaches in TRECVID 2007.