ImageCLEFcoral

Welcome to the 3rd edition of the Coral Task!

Description

Motivation

The increasing use of structure-from-motion photogrammetry for modelling large-scale environments from action cameras attached to drones has driven the next-generation of visualisation techniques that can be used in augmented and virtual reality headsets. It has also created a need to have such models labelled, with objects such as people, buildings, vehicles, terrain, etc. all essential for machine learning techniques to automatically identify as areas of interest and to label them appropriately. However, the complexity of the images makes impossible for human annotators to assess the contents of images on a large scale.
Advances in automatically annotating images for complexity and benthic composition have been promising, and we are interested in automatically identify areas of interest and to label them appropriately for monitoring coral reefs. Coral reefs are in danger of being lost within the next 30 years, and with them the ecosystems they support. This catastrophe will not only see the extinction of many marine species, but also create a humanitarian crisis on a global scale for the billions of humans who rely on reef services. By monitoring the changes and composition of coral reefs we can help prioritise conservation efforts.

New for 2021:

In its 3rd edition, the training and test data will form the complete set of images required to form a 3D reconstruction of the environment. This allows the participants to explore novel probabilistic computer vision techniques based around image overlap and transposition of data points. The training dataset contains images from 6 datasets from 4 locations. 1 subset will contain all the images to build the 3D model (see below) and 4 subsets will contain a partial collection. The test data will be the remaining images for each of the partial collections.

3D models for ImageCLEFcoral 2021

The images for each model was collected using a 5-camera array moving over the terrain. The images typically overlap each other by 60% and are likely to contain some of the same features of the landscape taken from many different angles. The images were aligned using Agisoft Metashape and processed into a 3D textured model using "medium" processing settings. The models are available to participants on request (as .obj files).

In addition, participants are encouraged to use the publicly available NOAA NCEI data and/or CoralNet to train their approaches. The CNETcategories_ImageCLEF_v1.xlsx file shows how to map NOAA categories to ImageCLEFcoral categories for training. NB: NOAA data is typically sparse pixel annotation over a large set of images, i.e, only 10 pixels per images are classified.

News

01.03.2021: development data released

Preliminary Schedule

16.11.2020: registration opens for all ImageCLEF tasks
~~21.01.2021~~01.03.2021: development data released
15.03.2021: test data release starts
~~07.05.2021~~17.05.2021: deadline for submitting the participants runs
14.05.2021: release of the processed results by the task organizers
28.05.2021: deadline for submission of working notes papers by the participants
11.06.2021: notification of acceptance of the working notes papers
02.07.2021: camera ready working notes papers
21-24.09.2021: CLEF 2021, Bucharest, Romania

Task Description

ImageCLEFmed Caption 2021 consists of two substaks:

Coral reef image annotation and localisation: https://www.aicrowd.com/challenges/imageclef-2021-coral-annotation-and-l...
Coral reef image pixel-wise parsing: https://www.aicrowd.com/challenges/imageclef-2021-coral-pixel-wise-parsing

Coral reef image annotation and localisation subtask

This subtask requires the participants to label the images with types of benthic substrate together with their bounding box in the image. Each image is provided with possible class types. For each image, participants will produce a set of bounding boxes, predicting the benthic substrate for each bounding box in the image.

Coral reef image pixel-wise parsing subtask

This subtask requires the participants to segment and parse each coral reef image into different image regions associated with benthic substrate types. For each image, segmentation algorithms will produce a semantic segmentation mask, predicting the semantic category for each pixel in the image.

Data

The data for this task originates from a growing, large-scale collection of images taken from coral reefs around the world as part of a coral reef monitoring project with the Marine Technology Research Unit at the University of Essex.
Substrates of the same type can have very different morphologies, color variation and patterns. Some of the images contain a white line (scientific measurement tape) that may occlude part of the entity. The quality of the images is variable, some are blurry, and some have poor color balance. This is representative of the Marine Technology Research Unit dataset and all images are useful for data analysis. The images contain annotations of the following 13 types of substrates: Hard Coral – Branching, Hard Coral – Submassive, Hard Coral – Boulder, Hard Coral – Encrusting, Hard Coral – Table, Hard Coral – Foliose, Hard Coral – Mushroom, Soft Coral, Soft Coral – Gorgonian, Sponge, Sponge – Barrel, Fire Coral – Millepora and Algae - Macro or Leaves.
The test data contains images from four different locations:

same location as training set
similar location to training set
geographically similar to training set
geographically distinct from training set

Evaluation methodology

The evaluation will be carry out using the PASCAL style metric of intersection over union (IoU), the area of intersection between the foreground in the
output segmentation and the foreground in the ground-truth segmentation, divided by the area of their union.
The final results will be presented both in terms of average performance over all images of all concepts, and also per concept performance over all images.

MAP_0.5 Is the localised Mean average precision (MAP) for each submitted method for using the performance measure of IoU >=0.5 of the ground truth

MAP_0 Is the image annotation average for each method with success if the concept is simply detected in the image without any localisation

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Accuracy per substrate The segmentation accuracy for a substrate will be assessed using the number of correctly labelled pixels of that substrate, divided by the number of pixels labelled with that class (in either the ground truth labelling or the inferred labelling).

Subtask #1: Coral reef image annotation and localisation task

Run	Group	MAP_0.5
138115	University of West Bohemia	0.121
137821	University of West Bohemia	0.105
139118	UAlbany	0.001

Accuracy per substrate
run_id	group	average	c_algae_macro_or_leaves	c_fire_coral_millepora	c_hard_coral_boulder	c_hard_coral_branching	c_hard_coral_encrusting	c_hard_coral_foliose	c_hard_coral_mushroom	c_hard_coral_submassive	c_hard_coral_table	c_soft_coral	c_soft_coral_gorgonian	c_sponge	c_sponge_barrel
139118	UAlbany	0.0002180	0.0000715	0.0000000	0.0000660	0.0004547	0.0001404	0.0000000	0.0000000	0.0000733	0.0001821	0.0001219	0.0000000	0.0007281	0.0000000
138115	University of West Bohemia	0.0003405	0.0002712	0.0000000	0.0003963	0.0002811	0.0003154	0.0000000	0.0000000	0.0005114	0.0002067	0.0007562	0.0000000	0.0003539	0.0000000
137821	University of West Bohemia	0.0003049	0.0002319	0.0000000	0.0002838	0.0003231	0.0001350	0.0000000	0.0013094	0.0004498	0.0001616	0.0006585	0.0000000	0.0003350	0.0000402

Subtask #2: Coral reef image pixel-wise parsing task

Run	Group	MAP_0.5
139084	University of West Bohemia	0.075
138389	MTRU	0.021
138443	MTRU	0.018
138411	MTRU	0.017
138449	MTRU	0.011

Accuracy per substrate
run_id	group	average	c_algae_macro_or_leaves	c_fire_coral_millepora	c_hard_coral_boulder	c_hard_coral_branching	c_hard_coral_encrusting	c_hard_coral_foliose	c_hard_coral_mushroom	c_hard_coral_submassive	c_hard_coral_table	c_soft_coral	c_soft_coral_gorgonian	c_sponge	c_sponge_barrel
139084	University of West Bohemia	0.061468	0.000001	0.000000	0.053853	0.110947	0.026151	0.000000	0.000000	0.027038	0.000075	0.504334	0.000000	0.016247	0.003293
138411	MTRU	0.019418	0.000000	0.000000	0.076005	0.005358	0.007285	0.000000	0.000000	0.010357	0.000000	0.002280	0.000015	0.000000	0.000000
138449	MTRU	0.029510	0.000000	0.000000	0.088272	0.055618	0.024292	0.000000	0.000000	0.000387	0.000000	0.000000	0.000000	0.000000	0.000000
138389	MTRU	0.027670	0.000000	0.000000	0.096070	0.010895	0.000166	0.000967	0.000000	0.030221	0.003534	0.000000	0.000000	0.000000	0.000000
138443	MTRU	0.025305	0.000272	0.000000	0.127873	0.022992	0.025947	0.000000	0.000000	0.002786	0.000000	0.000000	0.000000	0.000000	0.000000

Participant registration

Please refer to the general ImageCLEF registration instructions

Submission instructions

The submissions will be received through the crowdAI

system.
Participants will be permitted to submit up to 10 runs. External training data is allowed and encouraged.
Each system run will consist of a single ASCII plain text file. The results of each test set should be given in separate lines in the text file. The format of the text file is as follows:
[image_ID/document_ID] [results]

Coral reef image annotation and localisation

The results of each test set image should be given in separate lines, each line providing only up to 500 localised substrates. The format has characters to separate the elements, semicolon ‘;’ for the substrates, colon ':' for the confidence, comma ',' to separate multiple bounding boxes, and 'x' and '+' for the size-offset bounding box format, i.e.:

[image_ID];[substrate1] [[confidence1,1]:][width1,1]x[height1,1]+[xmin1,1]+[ymin1,1],[[confidence1,2]:][width1,2]x[height1,2]+[xmin1,2]+[ymin1,2],...;[substrate2] ...

[confidence] are floating point values 0-1 for which a higher value means a higher score.

For example, in the development set format (notice that there are 2 bounding boxes for substrate c_soft_coral):

2018_0714_112604_057 0 c_hard_coral_branching 1 891 540 1757 1143
2018_0714_112604_057 3 c_soft_coral 1 2724 1368 2825 1507
2018_0714_112604_057 4 c_soft_coral 1 2622 1576 2777 1731

In the submission format, it would be a line as:

2018_0714_112604_057;c_hard_coral_branching 0.6:867x 604+891+540;c_soft_coral 0.7:102x140+2724+2825,0.3:156x156+2622+1576

Coral reef image pixel-wise parsing subtask

Similar to subtask 1, the results of each test set image should be given in separate lines, each line providing only up to 500 localised substrates, with up to 500 coordinate localisations of the same substrate expected. The format has characters to separate the elements, semicolon ';' for the substrates, colon ':' for the confidence, comma ',' to separate multiple bounding polygons, and 'x' and '+' for the size-offset bounding polygon format, i.e.:

[image_ID];[substrate1] [[confidence1,1]:][x1,1]+[y1,1]+[x2,1]+[y2,1]+….+[xn,1]+[yn,1],[[confidence1,2][x1,2]+[y1,2]+[x2,2]+[y2,2]+….+[xn,2]+[yn,2];[substrate2] ...

[confidence] are floating point values 0-1 for which a higher value means a higher score and the [xi,yi] represents consecutive points.

For example, in the development set format (notice that there are 2 polygons for substrate c_soft_coral):

2018_0714_112604_057 0 c_hard_coral_branching 1 1757 833 1645 705 1559 598 1442 540 1249 593 1121 679 1020 705 998 844 891 967 966 1122 1137 1143 1324 1122 1468 1074 1655 978
2018_0714_112604_057 3 c_soft_coral 1 2804 1368 2745 1368 2724 1427 2729 1507 2809 1507 2825 1453
2018_0714_112604_057 4 c_soft_coral 1 2697 1576 2638 1592 2638 1608 2622 1667 2654 1694 2713 1731 2777 1731 2777 1635

In the submission format, it would be a line as:

2018_0714_112604_057;c_hard_coral_branching 0.6:1757+833+1645+705+1559+598+1442+540+1249+593+1121+679+1020+705+998+844+891+967+966+1122+1137+1143+1324+1122+1468+1074+1655+978;c_soft_coral 0.7:2804+1368+2745+1368+2724+1427+2729+1507+2809+1507+2825+1453,0.3:2697+1576+2638+1592+2638+1608+2622+1667+2654+1694+2713+1731+2777+1731+2777+1635

Participant registration

Please refer to the general ImageCLEF registration instructions

Submission instructions

The submissions will be received through the crowdAI

CEUR Working Notes

All participating teams with at least one graded submission, regardless of the score, should submit a CEUR working notes paper.
Official detailed instructions for the CLEF 2021 working notes can be found here: http://clef2021.clef-initiative.eu/index.php?page=Pages/instructions_for_authors.html

Citations

When referring to the ImageCLEF 2021 coral task general goals, general results, etc. please cite the following publication which will be published in September 2021:

Jon Chamberlain, Alba García Seco de Herrera ,Antonio Campello, Adrian Clark, Thomas A. Oliver and Hassan Moustahfid. Overview of the ImageCLEFcoral 20201Task: Coral Reef Image Annotation of a 3D environment, in Experimental IR Meets Multilinguality, Multimodality, and Interaction.CEUR Workshop Proceedings (CEUR- WS.org), Bucharest, Romania, September 21-24, 2021.

BibTex:
@Inproceedings{ImageCLEFcoraloverview2021,
author = {Chamberlain, Jon and Garc\'ia Seco de Herrera, Alba and Campello, Antonio and Clark, Adrian and Oliver, Thomas A. and Moustahfid, Hassan},
title = {Overview of the {ImageCLEFcoral} 20201Task: Coral Reef Image Annotation of a 3D environment}
booktitle = {CLEF2021 Working Notes},
series = {{CEUR} Workshop Proceedings},
year = {2021},
volume = {},
publisher = {CEUR-WS.org },
pages = {},
month = {September 21-24},
address = {Bucharest, Romania}
}

Contact

Jon Chamberlain <jchamb(at)essex.ac.uk>,University of Essex, UK
Thomas A. Oliver <thomas.oliver(at)noaa.gov>, NOAA - Pacific Islands Fisheries Science Center, USA
Hassan Moustahfid <hassan.moustahfid(at)noaa.gov>, NOAA/ US IOOS, USA
Antonio Campello <a.campello(at)wellcome.ac.uk>,Wellcome Trust, UK
Adrian Clark <alien(at)essex.ac.uk>,University of Essex, UK
Alba García Seco de Herrera <alba.garcia(at)essex.ac.uk>,University of Essex, UK

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Navigation

You are here

Welcome to the 3rd edition of the Coral Task!

Motivation

News

Preliminary Schedule

Task Description

Coral reef image annotation and localisation subtask

Coral reef image pixel-wise parsing subtask

Data

Evaluation methodology

Subtask #1: Coral reef image annotation and localisation task

Subtask #2: Coral reef image pixel-wise parsing task

Participant registration

Submission instructions

Coral reef image annotation and localisation

Coral reef image pixel-wise parsing subtask

Participant registration

Submission instructions

CEUR Working Notes

Citations

Contact