A collaborative approach for semantic time-based video annotation using gamification

Detalhes bibliográficos
Autor(a) principal: Viana, Paula
Data de Publicação: 2017
Outros Autores: Pinto, José Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/10779
Resumo: Efficient access to large scale video assets, may it be our life memories in our hard drive or a broadcaster archive which the company is eager to sell, requires content to be conveniently annotated. Manually annotating video content is, however, an intellectually expensive and time-consuming process. In this paper we argue that crowdsourcing, an approach that relies on a remote task force to perform activities that are costly or time-consuming using traditional methods, is a suitable alternative and we describe a solution based on gamification mechanisms for collaboratively collecting timed metadata. Tags introduced by registered players are validated based on a collaborative scoring mechanism that excludes erratic annotations. Voting mechanisms, enabling users to approve or refuse existing tags, provide an extra guarantee on the quality of the annotations. The sense of community is also created as users may watch the crowd’s favourite moments of the video provided by a summarization functionality. The system was tested with a pool of volunteers in order to evaluate the quality of the contributions. The results suggest that crowdsourced annotation can describe objects, persons, places, etc. correctly, as well as be very accurate in time.
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spelling A collaborative approach for semantic time-based video annotation using gamificationTaggingVideo annotationGamificationCrowdsourceMetadataEfficient access to large scale video assets, may it be our life memories in our hard drive or a broadcaster archive which the company is eager to sell, requires content to be conveniently annotated. Manually annotating video content is, however, an intellectually expensive and time-consuming process. In this paper we argue that crowdsourcing, an approach that relies on a remote task force to perform activities that are costly or time-consuming using traditional methods, is a suitable alternative and we describe a solution based on gamification mechanisms for collaboratively collecting timed metadata. Tags introduced by registered players are validated based on a collaborative scoring mechanism that excludes erratic annotations. Voting mechanisms, enabling users to approve or refuse existing tags, provide an extra guarantee on the quality of the annotations. The sense of community is also created as users may watch the crowd’s favourite moments of the video provided by a summarization functionality. The system was tested with a pool of volunteers in order to evaluate the quality of the contributions. The results suggest that crowdsourced annotation can describe objects, persons, places, etc. correctly, as well as be very accurate in time.SpringerOpenRepositório Científico do Instituto Politécnico do PortoViana, PaulaPinto, José Pedro2018-01-16T11:01:28Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/10779eng10.1186/s13673-017-0094-5info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:52:46Zoai:recipp.ipp.pt:10400.22/10779Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:31:12.421588Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A collaborative approach for semantic time-based video annotation using gamification
title A collaborative approach for semantic time-based video annotation using gamification
spellingShingle A collaborative approach for semantic time-based video annotation using gamification
Viana, Paula
Tagging
Video annotation
Gamification
Crowdsource
Metadata
title_short A collaborative approach for semantic time-based video annotation using gamification
title_full A collaborative approach for semantic time-based video annotation using gamification
title_fullStr A collaborative approach for semantic time-based video annotation using gamification
title_full_unstemmed A collaborative approach for semantic time-based video annotation using gamification
title_sort A collaborative approach for semantic time-based video annotation using gamification
author Viana, Paula
author_facet Viana, Paula
Pinto, José Pedro
author_role author
author2 Pinto, José Pedro
author2_role author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Viana, Paula
Pinto, José Pedro
dc.subject.por.fl_str_mv Tagging
Video annotation
Gamification
Crowdsource
Metadata
topic Tagging
Video annotation
Gamification
Crowdsource
Metadata
description Efficient access to large scale video assets, may it be our life memories in our hard drive or a broadcaster archive which the company is eager to sell, requires content to be conveniently annotated. Manually annotating video content is, however, an intellectually expensive and time-consuming process. In this paper we argue that crowdsourcing, an approach that relies on a remote task force to perform activities that are costly or time-consuming using traditional methods, is a suitable alternative and we describe a solution based on gamification mechanisms for collaboratively collecting timed metadata. Tags introduced by registered players are validated based on a collaborative scoring mechanism that excludes erratic annotations. Voting mechanisms, enabling users to approve or refuse existing tags, provide an extra guarantee on the quality of the annotations. The sense of community is also created as users may watch the crowd’s favourite moments of the video provided by a summarization functionality. The system was tested with a pool of volunteers in order to evaluate the quality of the contributions. The results suggest that crowdsourced annotation can describe objects, persons, places, etc. correctly, as well as be very accurate in time.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01T00:00:00Z
2018-01-16T11:01:28Z
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