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 language | English |
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Pages (from-to) | 1551-1557 |
Number of pages | 7 |
Journal | IEEE Transactions on Consumer Electronics |
Volume | 55 |
Issue number | 3 |
DOIs | |
Publication status | Published - 01 Aug 2009 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Media Technology