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Active Learning to Assist Annotation of Aerial Images in Environmental Surveys

Mathieu Laroze 1 Romain Dambreville 2, 1 Chloé Friguet 1 Ewa Kijak 3 Sébastien Lefèvre 1
1 OBELIX - Environment observation with complex imagery
2 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
GIPSA-DIS - Département Images et Signal, GIPSA-PSD - GIPSA Pôle Sciences des Données
3 LinkMedia - Creating and exploiting explicit links between multimedia fragments
IRISA-D6 - MEDIA ET INTERACTIONS, Inria Rennes – Bretagne Atlantique
Abstract : Nowadays, remote sensing technologies greatly ease environmental assessment using aerial images. Such data are most often analyzed by a manual operator, leading to costly and non scalable solutions. In the fields of both machine learning and image processing, many algorithms have been developed to fasten and automate this complex task. Their main common assumption is the need to have prior ground truth available. However, for field experts or engineers, manually labeling the objects requires a time-consuming and tedious process. Restating the labeling issue as a binary classification one, we propose a method to assist the costly annotation task by introducing an active learning process, considering a query-by-group strategy. Assuming that a comprehensive context may be required to assist the annotator with the labeling task of a single instance, the labels of all the instances of an image are indeed queried. A score based on instances distribution is defined to rank the images for annotation and an appropriate retraining step is derived to simultaneously reduce the interaction cost and improve the classifier performances at each iteration. A numerical study on real images is conducted to assess the algorithm performances. It highlights promising results regarding the classification rate along with the chosen retraining strategy and the number of interactions with the user.
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Contributor : Ewa Kijak <>
Submitted on : Friday, July 20, 2018 - 1:52:32 PM
Last modification on : Wednesday, May 13, 2020 - 4:30:04 PM
Document(s) archivé(s) le : Sunday, October 21, 2018 - 6:44:37 PM


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  • HAL Id : hal-01845487, version 1


Mathieu Laroze, Romain Dambreville, Chloé Friguet, Ewa Kijak, Sébastien Lefèvre. Active Learning to Assist Annotation of Aerial Images in Environmental Surveys. CBMI 2018 - International Conference on Content-Based Multimedia Indexing, Sep 2018, La Rochelle, France. pp.1-6. ⟨hal-01845487⟩



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