Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorPicek, Lukáš
dc.contributor.authorŠulc, Milan
dc.contributor.authorMatas, Jiří
dc.contributor.authorHeilmann-Clausen, Jacob
dc.date.accessioned2023-02-13T11:00:21Z-
dc.date.available2023-02-13T11:00:21Z-
dc.date.issued2022
dc.identifier.citationPICEK, L. ŠULC, M. MATAS, J. HEILMANN-CLAUSEN, J. Overview of FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem. In Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum. Bologna: CEUR-WS, 2022. s. 1970-1981. ISBN: neuvedeno , ISSN: 1613-0073cs
dc.identifier.isbnneuvedeno
dc.identifier.issn1613-0073
dc.identifier.uri2-s2.0-85136986038
dc.identifier.urihttp://hdl.handle.net/11025/51466
dc.format12 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherCEUR-WSen
dc.relation.ispartofseriesProceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forumen
dc.rights© authorsen
dc.titleOverview of FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problemen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe main goal of the new LifeCLEF challenge, FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem, was to provide an evaluation ground for end-to-end fungi species recognition in an open class set scenario. An AI-based fungi species recognition system deployed in the Atlas of Danish Fungi helps mycologists to collect valuable data and allows users to learn about fungi species identification. Advances in fungi recognition from images and metadata will allow continuous improvement of the system deployed in this citizen science project. The training set is based on the Danish Fungi 2020 dataset and contains 295,938 photographs of 1,604 species. For testing, we provided a collection of 59,420 expert-approved observations collected in 2021. The test set includes 1,165 species from the training set and 1,969 unknown species, leading to an open-set recognition problem. This paper provides (i) a description of the challenge task and datasets, (ii) a summary of the evaluation methodology, (iii) a review of the systems submitted by the participating teams, and (iv) a discussion of the challenge results. © 2022 Copyright for this paper by its authors.en
dc.subject.translatedclassificationen
dc.subject.translatedcomputer visionen
dc.subject.translatedfine grained visual categorizationen
dc.subject.translatedfungien
dc.subject.translatedFungiCLEFen
dc.subject.translatedLifeCLEFen
dc.subject.translatedmachine learningen
dc.subject.translatedmetadataen
dc.subject.translatedopen-set recognitionen
dc.subject.translatedspecies identificationen
dc.type.statusPeer-revieweden
dc.identifier.obd43937114
dc.project.IDSS05010008/Detekce, identifikace a monitoring živočichů pokročilými metodami počítačového viděnícs
dc.project.IDSGS-2022-017/Inteligentní metody strojového vnímání a porozumění 5cs
Vyskytuje se v kolekcích:Konferenční příspěvky / Conference papers (NTIS)
Konferenční příspěvky / Conference Papers (KKY)
OBD

Soubory připojené k záznamu:
Soubor VelikostFormát 
Picek_Matas_Heilmann-Clausen_Overview_of_Fungi_CLEF_2022.pdf6,57 MBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/51466

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikační činnost / Publications
  3. OBD