Wild Cetacea Identification using Image Metadata
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata
2017-04-01
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| Series: | Journal of Computer Science and Technology |
| Subjects: | |
| Online Access: | https://journal.info.unlp.edu.ar/JCST/article/view/447 |
| _version_ | 1819123210163060736 |
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| author | Débora Pollicelli Mariano Coscarella Claudio Delrieux |
| author_facet | Débora Pollicelli Mariano Coscarella Claudio Delrieux |
| author_sort | Débora Pollicelli |
| collection | DOAJ |
| 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 interobserver 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 223 different Commerson’s dolphins (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. |
| first_indexed | 2024-12-22T07:04:43Z |
| format | Article |
| id | doaj.art-549c090417fb43fbb7e1af2744937549 |
| institution | Directory Open Access Journal |
| issn | 1666-6046 1666-6038 |
| language | English |
| last_indexed | 2024-12-22T07:04:43Z |
| publishDate | 2017-04-01 |
| publisher | Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata |
| record_format | Article |
| series | Journal of Computer Science and Technology |
| spelling | doaj.art-549c090417fb43fbb7e1af27449375492022-12-21T18:34:42ZengPostgraduate Office, School of Computer Science, Universidad Nacional de La PlataJournal of Computer Science and Technology1666-60461666-60382017-04-0117017984227Wild Cetacea Identification using Image MetadataDébora Pollicelli0Mariano Coscarella1Claudio Delrieux2CESIMAR-CONICET, Centro para el Estudio de Sistemas Marinos, Consejo Nacional de Investigaciones Cient ́ıficas y T ́ecnicas, CCT CENPAT, Bv. Almirante Brown 2915, 9120, Puerto Madryn, Chubut, ArgentinaCESIMAR-CONICET, Centro para el Estudio de Sistemas Marinos, Consejo Nacional de Investigaciones Cient ́ıficas y T ́ecnicas, CCT CENPAT, Bv. Almirante Brown 2915, 9120, Puerto Madryn, Chubut, ArgentinaLaboratorio de Ciencias de las Imágenes, Departamento de Ingenier ́ıa El ́ectrica y Computadoras, Universidad Nacional del Sur y CONICET, 8000 Bahía Blanca, Argentina –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 interobserver 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 223 different Commerson’s dolphins (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.https://journal.info.unlp.edu.ar/JCST/article/view/447machine learningphoto-identificationcetaceanscommerson’s dolphins |
| spellingShingle | Débora Pollicelli Mariano Coscarella Claudio Delrieux Wild Cetacea Identification using Image Metadata Journal of Computer Science and Technology machine learning photo-identification cetaceans commerson’s dolphins |
| title | Wild Cetacea Identification using Image Metadata |
| title_full | Wild Cetacea Identification using Image Metadata |
| title_fullStr | Wild Cetacea Identification using Image Metadata |
| title_full_unstemmed | Wild Cetacea Identification using Image Metadata |
| title_short | Wild Cetacea Identification using Image Metadata |
| title_sort | wild cetacea identification using image metadata |
| topic | machine learning photo-identification cetaceans commerson’s dolphins |
| url | https://journal.info.unlp.edu.ar/JCST/article/view/447 |
| work_keys_str_mv | AT deborapollicelli wildcetaceaidentificationusingimagemetadata AT marianocoscarella wildcetaceaidentificationusingimagemetadata AT claudiodelrieux wildcetaceaidentificationusingimagemetadata |