Biodiversity information retrieval through large scale content-based identification: a long-term evaluation

Joly Alexis, Goeau Hervé, Glotin Hervé, Spampinato Concetto, Bonnet Pierre, Vellinga Willem-Pier, Lombardo Jean-Christophe, Planqué Robert, Palazzo Simone, Müller Henning. 2019. Biodiversity information retrieval through large scale content-based identification: a long-term evaluation. In : Information retrieval evaluation in a changing world: lessons learned from 20 years of CLEF. Ferro Nicola (ed.), Peters Carol (ed.). Cham : Springer, 389-413. (The Information Retrieval Series, 41, 41) ISBN 978-3-030-22947-4

https://doi.org/10.1007/978-3-030-22948-1_16

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Résumé : Identifying and naming living plants or animals is usually impossible for the general public and often a difficult task for professionals and naturalists. Bridging this gap is a key challenge towards enabling effective biodiversity information retrieval systems. This taxonomic gap was actually already identified as one of the main ecological challenges to be solved during the Rio de Janeiro United Nations “Earth Summit” in 1992. Since 2011, the LifeCLEF challenges conducted in the context of the CLEF evaluation forum have been boosting and evaluating the advances in this domain. Data collections with an unprecedented volume and diversity have been shared with the scientific community to allow repeatable and long-term experiments. This paper describes the methodology of the conducted evaluation campaigns as well as providing a synthesis of the main results and lessons learned along the years.

Mots-clés libres : Biodiversity multimedia data, Evaluation, Machine learning, Benchmark, Species identification

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