Overview of ImageCLEFcoral 2019 task

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.