An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms

Autores
Balmaceda, Leandro; Diaz, Ariel I.; Rostagno, Adrián; Aggio, Santiago L.; Blanco, Anibal; Iparraguirre, Javier
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The creation, consumption, and manipulation of video play a central role in everyday life as the amount of video data is growing at an exponential rate. Video summarization consists on producing a condensed output from a video that allows humans to rapidly understand and browse the content of the original source. Although there are several evaluation approaches proposed in the literature, multiple challenges make the quantitative evaluation of a summarization a complex process. In this paper we present a completely open video summarization evaluation framework that is compatible with existing datasets and published results. Standard metrics are considered and a new metric that captures unbalanced-class video summarization evaluation is proposed. Two legacy datasets are integrated in a standard format. Finally, new quantitative results based on already published algorithms are presented.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Algorithms
Video summarization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/89188
Acceder
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network_acronym_str SEDICI
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network_name_str SEDICI (UNLP)
spelling An Open Source Quantitative Evaluation Framework for Automatic Video Summarization AlgorithmsBalmaceda, LeandroDiaz, Ariel I.Rostagno, AdriánAggio, Santiago L.Blanco, AnibalIparraguirre, JavierCiencias InformáticasAlgorithmsVideo summarizationThe creation, consumption, and manipulation of video play a central role in everyday life as the amount of video data is growing at an exponential rate. Video summarization consists on producing a condensed output from a video that allows humans to rapidly understand and browse the content of the original source. Although there are several evaluation approaches proposed in the literature, multiple challenges make the quantitative evaluation of a summarization a complex process. In this paper we present a completely open video summarization evaluation framework that is compatible with existing datasets and published results. Standard metrics are considered and a new metric that captures unbalanced-class video summarization evaluation is proposed. Two legacy datasets are integrated in a standard format. Finally, new quantitative results based on already published algorithms are presented.Sociedad Argentina de Informática e Investigación Operativa2019-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf58-63http://sedici.unlp.edu.ar/handle/10915/89188enginfo:eu-repo/semantics/altIdentifier/issn/2683-8990info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:50:33Zoai:sedici.unlp.edu.ar:10915/89188Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:50:33.85SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms
title An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms
spellingShingle An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms
Balmaceda, Leandro
Ciencias Informáticas
Algorithms
Video summarization
title_short An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms
title_full An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms
title_fullStr An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms
title_full_unstemmed An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms
title_sort An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms
dc.creator.none.fl_str_mv Balmaceda, Leandro
Diaz, Ariel I.
Rostagno, Adrián
Aggio, Santiago L.
Blanco, Anibal
Iparraguirre, Javier
author Balmaceda, Leandro
author_facet Balmaceda, Leandro
Diaz, Ariel I.
Rostagno, Adrián
Aggio, Santiago L.
Blanco, Anibal
Iparraguirre, Javier
author_role author
author2 Diaz, Ariel I.
Rostagno, Adrián
Aggio, Santiago L.
Blanco, Anibal
Iparraguirre, Javier
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Algorithms
Video summarization
topic Ciencias Informáticas
Algorithms
Video summarization
dc.description.none.fl_txt_mv The creation, consumption, and manipulation of video play a central role in everyday life as the amount of video data is growing at an exponential rate. Video summarization consists on producing a condensed output from a video that allows humans to rapidly understand and browse the content of the original source. Although there are several evaluation approaches proposed in the literature, multiple challenges make the quantitative evaluation of a summarization a complex process. In this paper we present a completely open video summarization evaluation framework that is compatible with existing datasets and published results. Standard metrics are considered and a new metric that captures unbalanced-class video summarization evaluation is proposed. Two legacy datasets are integrated in a standard format. Finally, new quantitative results based on already published algorithms are presented.
Sociedad Argentina de Informática e Investigación Operativa
description The creation, consumption, and manipulation of video play a central role in everyday life as the amount of video data is growing at an exponential rate. Video summarization consists on producing a condensed output from a video that allows humans to rapidly understand and browse the content of the original source. Although there are several evaluation approaches proposed in the literature, multiple challenges make the quantitative evaluation of a summarization a complex process. In this paper we present a completely open video summarization evaluation framework that is compatible with existing datasets and published results. Standard metrics are considered and a new metric that captures unbalanced-class video summarization evaluation is proposed. Two legacy datasets are integrated in a standard format. Finally, new quantitative results based on already published algorithms are presented.
publishDate 2019
dc.date.none.fl_str_mv 2019-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/89188
url http://sedici.unlp.edu.ar/handle/10915/89188
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2683-8990
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
dc.format.none.fl_str_mv application/pdf
58-63
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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