Wild Cetacean Identification using Image Metadata
- Autores
- Pollicelli, Débora; Coscarella, Mariano; Delrieux, Claudio
- Año de publicación
- 2016
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Identification of individuals in marine species, especially in Cetacea, is a critical task in several biological and ecological endeavours. Most of the times this is performed through human-assisted matching within a set of pictures taken in different campaigns during several years and spread around wide geographical regions. This requires that the scientists perform laborious tasks in searching through archives of images, demanding a significant cognitive burden which may be prone to intra and inter observer operational errors. On the other hand, additional available information, in particular the metadata associated to every image, is not fully taken advantage of. The present work presents the result of applying machine learning techniques over the metadata of archives of images as an aid in the process of manual identification. The method was tested on a database containing several pictures of 230 different Commerson’s dolp hins (Cephalorhynchus commersoni) taken over a span of seven years. A supervised classifier trained with identifications made by the researchers was able to identify correctly above 90% of the individuals on the test set using only the metadata present in the image files. This reduces significantly the number of images to be manually compared, and therefore the time and errors associated with the assisted identification process.
XIII Workshop Bases de datos y Minería de Datos (WBDMD).
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
machine learning
photo-identification - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/56759
Ver los metadatos del registro completo
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Wild Cetacean Identification using Image MetadataPollicelli, DéboraCoscarella, MarianoDelrieux, ClaudioCiencias Informáticasmachine learningphoto-identificationIdentification of individuals in marine species, especially in Cetacea, is a critical task in several biological and ecological endeavours. Most of the times this is performed through human-assisted matching within a set of pictures taken in different campaigns during several years and spread around wide geographical regions. This requires that the scientists perform laborious tasks in searching through archives of images, demanding a significant cognitive burden which may be prone to intra and inter observer operational errors. On the other hand, additional available information, in particular the metadata associated to every image, is not fully taken advantage of. The present work presents the result of applying machine learning techniques over the metadata of archives of images as an aid in the process of manual identification. The method was tested on a database containing several pictures of 230 different Commerson’s dolp hins (Cephalorhynchus commersoni) taken over a span of seven years. A supervised classifier trained with identifications made by the researchers was able to identify correctly above 90% of the individuals on the test set using only the metadata present in the image files. This reduces significantly the number of images to be manually compared, and therefore the time and errors associated with the assisted identification process.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI)2016-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf765-774http://sedici.unlp.edu.ar/handle/10915/56759enginfo:eu-repo/semantics/reference/hdl/10915/55718info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:06:08Zoai:sedici.unlp.edu.ar:10915/56759Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:06:08.401SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Wild Cetacean Identification using Image Metadata |
title |
Wild Cetacean Identification using Image Metadata |
spellingShingle |
Wild Cetacean Identification using Image Metadata Pollicelli, Débora Ciencias Informáticas machine learning photo-identification |
title_short |
Wild Cetacean Identification using Image Metadata |
title_full |
Wild Cetacean Identification using Image Metadata |
title_fullStr |
Wild Cetacean Identification using Image Metadata |
title_full_unstemmed |
Wild Cetacean Identification using Image Metadata |
title_sort |
Wild Cetacean Identification using Image Metadata |
dc.creator.none.fl_str_mv |
Pollicelli, Débora Coscarella, Mariano Delrieux, Claudio |
author |
Pollicelli, Débora |
author_facet |
Pollicelli, Débora Coscarella, Mariano Delrieux, Claudio |
author_role |
author |
author2 |
Coscarella, Mariano Delrieux, Claudio |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas machine learning photo-identification |
topic |
Ciencias Informáticas machine learning photo-identification |
dc.description.none.fl_txt_mv |
Identification of individuals in marine species, especially in Cetacea, is a critical task in several biological and ecological endeavours. Most of the times this is performed through human-assisted matching within a set of pictures taken in different campaigns during several years and spread around wide geographical regions. This requires that the scientists perform laborious tasks in searching through archives of images, demanding a significant cognitive burden which may be prone to intra and inter observer operational errors. On the other hand, additional available information, in particular the metadata associated to every image, is not fully taken advantage of. The present work presents the result of applying machine learning techniques over the metadata of archives of images as an aid in the process of manual identification. The method was tested on a database containing several pictures of 230 different Commerson’s dolp hins (Cephalorhynchus commersoni) taken over a span of seven years. A supervised classifier trained with identifications made by the researchers was able to identify correctly above 90% of the individuals on the test set using only the metadata present in the image files. This reduces significantly the number of images to be manually compared, and therefore the time and errors associated with the assisted identification process. XIII Workshop Bases de datos y Minería de Datos (WBDMD). Red de Universidades con Carreras en Informática (RedUNCI) |
description |
Identification of individuals in marine species, especially in Cetacea, is a critical task in several biological and ecological endeavours. Most of the times this is performed through human-assisted matching within a set of pictures taken in different campaigns during several years and spread around wide geographical regions. This requires that the scientists perform laborious tasks in searching through archives of images, demanding a significant cognitive burden which may be prone to intra and inter observer operational errors. On the other hand, additional available information, in particular the metadata associated to every image, is not fully taken advantage of. The present work presents the result of applying machine learning techniques over the metadata of archives of images as an aid in the process of manual identification. The method was tested on a database containing several pictures of 230 different Commerson’s dolp hins (Cephalorhynchus commersoni) taken over a span of seven years. A supervised classifier trained with identifications made by the researchers was able to identify correctly above 90% of the individuals on the test set using only the metadata present in the image files. This reduces significantly the number of images to be manually compared, and therefore the time and errors associated with the assisted identification process. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-10 |
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/10915/56759 |
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http://sedici.unlp.edu.ar/handle/10915/56759 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf 765-774 |
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