Automatic video summarization using the optimum-path forest unsupervised classifier

César Castelo-Fernández; Guillermo Calderón-Ruiz

doi:10.1007/978-3-319-25751-8_91

Automatic video summarization using the optimum-path forest unsupervised classifier

César Castelo-Fernández, Guillermo Calderón-Ruiz

Professional School of Systems Engineering

Universidad Católica de Santa María

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

In this paper a novel method for video summarization is presented, which uses a color-based feature extraction technique and a graph-based clustering technique. One major advantage of this method is that it is parameter-free, that is, we do not need to define neither the number of shots or a consecutive-frames dissimilarity threshold. The results have shown that the method is both effective and efficient in processing videos containing several thousands of frames, obtaining very meaningful summaries in a quick way.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Alvaro Pardo, Josef Kittler
Publisher	Springer Verlag
Pages	760-767
Number of pages	8
ISBN (Print)	9783319257501
DOIs	https://doi.org/10.1007/978-3-319-25751-8_91
State	Published - 2015
Event	20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015 - Montevideo, Uruguay Duration: 9 Nov 2015 → 12 Nov 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9423
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015
Country/Territory	Uruguay
City	Montevideo
Period	9/11/15 → 12/11/15

Keywords

Clustering
Optimum-path forest classifier
Shot detection
Video processing
Video summarization

Access to Document

10.1007/978-3-319-25751-8_91

Cite this

Castelo-Fernández, C., & Calderón-Ruiz, G. (2015). Automatic video summarization using the optimum-path forest unsupervised classifier. In A. Pardo, & J. Kittler (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 760-767). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9423). Springer Verlag. https://doi.org/10.1007/978-3-319-25751-8_91

Castelo-Fernández, César ; Calderón-Ruiz, Guillermo. / Automatic video summarization using the optimum-path forest unsupervised classifier. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Alvaro Pardo ; Josef Kittler. Springer Verlag, 2015. pp. 760-767 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper a novel method for video summarization is presented, which uses a color-based feature extraction technique and a graph-based clustering technique. One major advantage of this method is that it is parameter-free, that is, we do not need to define neither the number of shots or a consecutive-frames dissimilarity threshold. The results have shown that the method is both effective and efficient in processing videos containing several thousands of frames, obtaining very meaningful summaries in a quick way.",

keywords = "Clustering, Optimum-path forest classifier, Shot detection, Video processing, Video summarization",

author = "C{\'e}sar Castelo-Fern{\'a}ndez and Guillermo Calder{\'o}n-Ruiz",

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Castelo-Fernández, C & Calderón-Ruiz, G 2015, Automatic video summarization using the optimum-path forest unsupervised classifier. in A Pardo & J Kittler (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9423, Springer Verlag, pp. 760-767, 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015, Montevideo, Uruguay, 9/11/15. https://doi.org/10.1007/978-3-319-25751-8_91

Automatic video summarization using the optimum-path forest unsupervised classifier. / Castelo-Fernández, César; Calderón-Ruiz, Guillermo.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Alvaro Pardo; Josef Kittler. Springer Verlag, 2015. p. 760-767 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9423).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic video summarization using the optimum-path forest unsupervised classifier

AU - Castelo-Fernández, César

AU - Calderón-Ruiz, Guillermo

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - In this paper a novel method for video summarization is presented, which uses a color-based feature extraction technique and a graph-based clustering technique. One major advantage of this method is that it is parameter-free, that is, we do not need to define neither the number of shots or a consecutive-frames dissimilarity threshold. The results have shown that the method is both effective and efficient in processing videos containing several thousands of frames, obtaining very meaningful summaries in a quick way.

AB - In this paper a novel method for video summarization is presented, which uses a color-based feature extraction technique and a graph-based clustering technique. One major advantage of this method is that it is parameter-free, that is, we do not need to define neither the number of shots or a consecutive-frames dissimilarity threshold. The results have shown that the method is both effective and efficient in processing videos containing several thousands of frames, obtaining very meaningful summaries in a quick way.

KW - Clustering

KW - Optimum-path forest classifier

KW - Shot detection

KW - Video processing

KW - Video summarization

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DO - 10.1007/978-3-319-25751-8_91

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SN - 9783319257501

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 760

EP - 767

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Pardo, Alvaro

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PB - Springer Verlag

T2 - 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015

Y2 - 9 November 2015 through 12 November 2015

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Castelo-Fernández C, Calderón-Ruiz G. Automatic video summarization using the optimum-path forest unsupervised classifier. In Pardo A, Kittler J, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2015. p. 760-767. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-25751-8_91

Automatic video summarization using the optimum-path forest unsupervised classifier

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