Hierarchical video summarization in reference subspace

Richard Jiang, Abdul Sadka, Danny Crookes

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity, and the sub-shot level and scene level representative frames are then summarized by using k-mean clustering. The experiment is carried on both test videos and movies, and the results show that in comparison with a similar approach using latent semantic analysis, the proposed approach using Laplacian Eigenmap can achieve a better recall rate in keyframe detection, and gives an efficient hierarchical summarization at sub shot, shot and scene levels subsequently.
Original languageEnglish
Pages (from-to)1551-1557
JournalIEEE Transactions on Consumer Electronics
Volume55
Issue number3
DOIs
Publication statusPublished - 2009

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