Dynamic selection and effective compression of key frames for video abstraction

Xu Dong Zhang, Tie Yan Liu, Kwok Tung Lo, Jian Feng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

44 Citations (Scopus)

Abstract

This paper reports on a new key frame based video abstraction method. With our method, a video sequence is first segmented into a number of video shots. Several key frames are selected in each shot using a dynamic selection technique. For these key frames, a motion-based clustering algorithm is applied so that key frames in the same cluster are alike in sense of motion compensation error, while those from different clusters are quit dissimilar. Then a novel cluster-based coding scheme is developed for efficient representation of the key frames. Simulations show that the proposed method can select key frames according to the dynamics of a video sequence and abstract the video with different levels of scalability.
Original languageEnglish
Pages (from-to)1523-1532
Number of pages10
JournalPattern Recognition Letters
Volume24
Issue number9-10
DOIs
Publication statusPublished - 1 Jun 2003

Keywords

  • Clustering and video abstraction
  • Key frame selection
  • Motion compensation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

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