An open source quantitative evaluation framework for automatic video summarization algorithms

Autores
Balmaceda, Leandro; Diaz, Ariel I.; Rostagno, Adrian; Aggio, Santiago Lujan; Blanco, Anibal Manuel; 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.
Fil: Balmaceda, Leandro. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina
Fil: Diaz, Ariel I.. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina
Fil: Rostagno, Adrian. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina
Fil: Aggio, Santiago Lujan. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Iparraguirre, Javier. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina
SAIV - Simposio Argentino de Imágenes y Visión
Salta
Argentina
Sociedad Argentina de Informática
Universidad Nacional de Salta
Materia
COMPUTER VISION
VIDEO SUMMARIZATION
MACHINE LEARNING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/159309
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/159309
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An open source quantitative evaluation framework for automatic video summarization algorithmsBalmaceda, LeandroDiaz, Ariel I.Rostagno, AdrianAggio, Santiago LujanBlanco, Anibal ManuelIparraguirre, JavierCOMPUTER VISIONVIDEO SUMMARIZATIONMACHINE LEARNINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The 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.Fil: Balmaceda, Leandro. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; ArgentinaFil: Diaz, Ariel I.. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; ArgentinaFil: Rostagno, Adrian. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; ArgentinaFil: Aggio, Santiago Lujan. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Iparraguirre, Javier. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; ArgentinaSAIV - Simposio Argentino de Imágenes y VisiónSaltaArgentinaSociedad Argentina de InformáticaUniversidad Nacional de SaltaUniversidad Nacional de Salta2019info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectConferenciaBookhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/159309An open source quantitative evaluation framework for automatic video summarization algorithms; SAIV - Simposio Argentino de Imágenes y Visión; Salta; Argentina; 2019; 58-632683-8990CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://48jaiio.sadio.org.arinfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/issue/archiveNacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:57:25Zoai:ri.conicet.gov.ar:11336/159309instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:57:26.271CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
COMPUTER VISION
VIDEO SUMMARIZATION
MACHINE LEARNING
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, Adrian
Aggio, Santiago Lujan
Blanco, Anibal Manuel
Iparraguirre, Javier
author Balmaceda, Leandro
author_facet Balmaceda, Leandro
Diaz, Ariel I.
Rostagno, Adrian
Aggio, Santiago Lujan
Blanco, Anibal Manuel
Iparraguirre, Javier
author_role author
author2 Diaz, Ariel I.
Rostagno, Adrian
Aggio, Santiago Lujan
Blanco, Anibal Manuel
Iparraguirre, Javier
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv COMPUTER VISION
VIDEO SUMMARIZATION
MACHINE LEARNING
topic COMPUTER VISION
VIDEO SUMMARIZATION
MACHINE LEARNING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
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.
Fil: Balmaceda, Leandro. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina
Fil: Diaz, Ariel I.. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina
Fil: Rostagno, Adrian. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina
Fil: Aggio, Santiago Lujan. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Iparraguirre, Javier. Universidad Tecnológica Nacional. Facultad Regional Bahía Blanca; Argentina
SAIV - Simposio Argentino de Imágenes y Visión
Salta
Argentina
Sociedad Argentina de Informática
Universidad Nacional de Salta
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
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Conferencia
Book
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/159309
An open source quantitative evaluation framework for automatic video summarization algorithms; SAIV - Simposio Argentino de Imágenes y Visión; Salta; Argentina; 2019; 58-63
2683-8990
CONICET Digital
CONICET
url http://hdl.handle.net/11336/159309
identifier_str_mv An open source quantitative evaluation framework for automatic video summarization algorithms; SAIV - Simposio Argentino de Imágenes y Visión; Salta; Argentina; 2019; 58-63
2683-8990
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://48jaiio.sadio.org.ar
info:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/issue/archive
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.coverage.none.fl_str_mv Nacional
dc.publisher.none.fl_str_mv Universidad Nacional de Salta
publisher.none.fl_str_mv Universidad Nacional de Salta
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269461093023744
score 13.13397

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