Video key frame extraction through dynamic Delaunay clustering with a structural constraint
Key frame based video summarization has emerged as an important area of research for the multimedia community. Video key frames enable an user to access any video in a friendly and meaningful way. In this paper, we propose an automated method of video key frame extraction using dynamic Delaunay grap...
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| Published in | Journal of visual communication and image representation Vol. 24; no. 7; pp. 1212 - 1227 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier Inc
01.10.2013
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1047-3203 1095-9076 |
| DOI | 10.1016/j.jvcir.2013.08.003 |
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| Abstract | Key frame based video summarization has emerged as an important area of research for the multimedia community. Video key frames enable an user to access any video in a friendly and meaningful way. In this paper, we propose an automated method of video key frame extraction using dynamic Delaunay graph clustering via an iterative edge pruning strategy. A structural constraint in form of a lower limit on the deviation ratio of the graph vertices further improves the video summary. We also employ an information-theoretic pre-sampling where significant valleys in the mutual information profile of the successive frames in a video are used to capture more informative frames. Various video key frame visualization techniques for efficient video browsing and navigation purposes are incorporated. A comprehensive evaluation on 100 videos from the Open Video and YouTube databases using both objective and subjective measures demonstrate the superiority of our key frame extraction method. |
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| AbstractList | Key frame based video summarization has emerged as an important area of research for the multimedia community. Video key frames enable an user to access any video in a friendly and meaningful way. In this paper, we propose an automated method of video key frame extraction using dynamic Delaunay graph clustering via an iterative edge pruning strategy. A structural constraint in form of a lower limit on the deviation ratio of the graph vertices further improves the video summary. We also employ an information-theoretic pre-sampling where significant valleys in the mutual information profile of the successive frames in a video are used to capture more informative frames. Various video key frame visualization techniques for efficient video browsing and navigation purposes are incorporated. A comprehensive evaluation on 100 videos from the Open Video and YouTube databases using both objective and subjective measures demonstrate the superiority of our key frame extraction method. |
| Author | Panda, Rameswar Kuanar, Sanjay K. Chowdhury, Ananda S. |
| Author_xml | – sequence: 1 givenname: Sanjay K. surname: Kuanar fullname: Kuanar, Sanjay K. email: sanjay.kuanar@gmail.com – sequence: 2 givenname: Rameswar surname: Panda fullname: Panda, Rameswar email: rameswar183@gmail.com – sequence: 3 givenname: Ananda S. surname: Chowdhury fullname: Chowdhury, Ananda S. email: aschowdhury@etce.jdvu.ac.in |
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| Keywords | Delaunay graphs Edge pruning Key frame visualization Deviation ratio Feature extraction Video summarization Clustering Information-theoretic pre-sampling Visualization Video signal Iterative method Successive frame Video databases Graph connectivity Selection criterion Pruning(tree) Sampling key frame Pattern extraction Multimedia Lower bound Computer vision Abstract Cluster Social network Delaunay triangulation Information browsing Web site Mutual information Information theory |
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| SubjectTerms | Applied sciences Artificial intelligence Clustering Computer science; control theory; systems Computer systems and distributed systems. User interface Data processing. List processing. Character string processing Delaunay graphs Deviation ratio Edge pruning Exact sciences and technology Feature extraction Information retrieval. Graph Information-theoretic pre-sampling Key frame visualization Memory organisation. Data processing Pattern recognition. Digital image processing. Computational geometry Software Theoretical computing Video summarization |
| Title | Video key frame extraction through dynamic Delaunay clustering with a structural constraint |
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