An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms

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 or...

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Detalles Bibliográficos
Autores principales: Balmaceda, Leandro, Diaz, Ariel I., Rostagno, Adrián, Aggio, Santiago L., Blanco, Anibal, Iparraguirre, Javier
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2019
Materias:
Ciencias Informáticas
Algorithms
Video summarization
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/89188
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-89188
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Algorithms
Video summarization
spellingShingle Ciencias Informáticas
Algorithms
Video summarization
Balmaceda, Leandro
Diaz, Ariel I.
Rostagno, Adrián
Aggio, Santiago L.
Blanco, Anibal
Iparraguirre, Javier
An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms
topic_facet Ciencias Informáticas
Algorithms
Video summarization
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.
format Objeto de conferencia
Objeto de conferencia
author Balmaceda, Leandro
Diaz, Ariel I.
Rostagno, Adrián
Aggio, Santiago L.
Blanco, Anibal
Iparraguirre, Javier
author_facet Balmaceda, Leandro
Diaz, Ariel I.
Rostagno, Adrián
Aggio, Santiago L.
Blanco, Anibal
Iparraguirre, Javier
author_sort Balmaceda, Leandro
title An Open Source Quantitative Evaluation Framework for Automatic Video Summarization Algorithms
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 open source quantitative evaluation framework for automatic video summarization algorithms
publishDate 2019
url http://sedici.unlp.edu.ar/handle/10915/89188
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