Research Repository

Overview of the ImageCLEFcoral 2020 Task: Automated Coral Reef Image Annotation

Chamberlain, Jon and Campello, Antonio and Wright, Jessica P and Clift, Louis G and Clark, Adrian and García Seco de Herrera, Alba (2020) Overview of the ImageCLEFcoral 2020 Task: Automated Coral Reef Image Annotation. In: Working Notes of CLEF 2020 - Conference and Labs of the Evaluation Forum, 2020-09-22 - 2020-09-25, Thessaloniki, Greece.

ImageCLEFcoral2020_overview.pdf - Published Version
Available under License Creative Commons Attribution.

Download (16MB) | Preview


This paper presents an overview of the ImageCLEFcoral 2020 task that was organised as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2020. The task addresses the problem of automatically segmenting and labelling a collection of underwater images that can be used in combination to create 3D models for the monitoring of coral reefs. The data set comprises 440 human-annotated training images, with 12,082 hand-annotated substrates, from a single geographical region. The test set comprises a further 400 test images, with 8,640 substrates annotated, from four geographical regions ranging in geographical similarity and ecological connectedness to the training data (100 images per subset). 15 teams registered, of which 4 teams submitted 53 runs. The majority of submissions used deep neural networks, generally convolutional ones. Participants’ entries showed that some level of automatically annotating corals and benthic substrates was possible, despite this being a difficult task due to the variation of colour, texture and morphology between and within classification types.

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: 27 Sep 2022 13:12
Last Modified: 27 Sep 2022 13:12

Actions (login required)

View Item View Item