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Data annotation with active learning: application to environmental surveys

Abstract : An active learning framework is introduced to deal with reducing the annotation cost for aerial images in environmental surveys. The selection of the queried instances at each step of the active process is here constrained by requiring that they belong to a group, an image (or a part of it) in our case. A score to rank the images and identify the one that should be annotated at each iteration is defined, based on both classifier uncertainty and performances. The performances of several strategies regarding the interaction gain are discussed based on an experiment on real image data collected for an environmental survey.
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Contributor : Chloé Friguet Connect in order to contact the contributor
Submitted on : Wednesday, November 24, 2021 - 10:22:14 AM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Friday, February 25, 2022 - 6:27:45 PM


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  • HAL Id : hal-03445740, version 1


Chloé Friguet, Romain Dambreville, Ewa Kijak, Mathieu Laroze, Sébastien Lefèvre. Data annotation with active learning: application to environmental surveys. JDS 2020 - 52èmes Journées de Statistique de la Société Française de Statistique (SFdS), May 2020, Nice, France. pp.1-6. ⟨hal-03445740⟩



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