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Overview of ImageCLEFcoral 2019 task

Chamberlain, J and Campello, A and Wright, J and Clift, L and Clark, A and Seco De Herrera, AG (2019) Overview of ImageCLEFcoral 2019 task. In: CLEF 2019 - Conference and Labs of the Evaluation Forum, 2019-09-09 - 2019-09-12, Lugano, Switzerland.

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Understanding the composition of species in ecosystems on a large scale is key to developing effective solutions for marine conservation, hence there is a need to classify imagery automatically and rapidly. In 2019, ImageCLEF proposed for the first time the ImageCLEFcoral task. The task requires participants to automatically annotate and localize benthic substrate (such as hard coral, soft coral, algae and sponge) in a collection of images originating from a growing, large-scale dataset from coral reefs around the world as part of monitoring programmes. In its first edition, five groups participated submitting 20 runs using a variety of machine learning and deep learning approaches. Best runs achieved 0.24 in the annotation and localisation subtask and 0.04 on the pixel-wise parsing subtask in terms of MAP 0.5 IoU scores which measures the Mean Average Precision (MAP) when using the performance measure of Intersection over Union (IoU) bigger to 0.5 of the ground truth.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: CEUR Workshop Proceedings
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 22 Jan 2020 14:59
Last Modified: 23 Sep 2022 19:34

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