ImageCLEF 2021 Coral - Pixel-wise parsing
Note: ImageCLEF Coral 2021 is divided into 2 subtasks (challenges). This is the Pixel-wise Parsing challenge. For information on the Annotation and Localisation challenge click here. Both challenges share the same dataset, so registering for one of these challenges will automatically give you access to the other one.
Note: Do not forget to read the Rules section on this page. Pressing the red Participate button leads you to a page where you have to agree with those rules. You will not be able to submit any results before agreeing with the rules.
Note: Before trying to submit results, read the Submission instructions section on this page.
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.
This task 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.
News 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 here ) and 4 subsets will contain a partial collection. The test data will be the remaining images for each of the partial collections.
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.
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
In addition, participants are encourage to use the publicly available NOAA NCEI data to train their approaches.
The data can be downloaded from the “Dataset” tab and will be made available on:
04.02.2019 Training data
15.03.2019 Test data
As soon as the data is released it will be available under the "Resources" tab.
As soon as the submission is open, you will find a “Create Submission” button on this page (next to the tabs).
Before being allowed to submit your results, you have to first press the red participate button, which leads you to a page where you have to accept the challenge's rules.
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:
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 bounding polygons should not self-intersect . 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.:
[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
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 in terms of average performance over all images of all concepts.
MAP_0.5Overlap Is the localised Mean average precision (MAP) for each submitted method for using the performance measure of IoU >=0.5 of the ground truth based on polygons (not on bounding boxes).
Further evaluation will be provided at https://www.imageclef.org/2020/coral including the following measures:
MAP_0.0Overlap Is the image annotation average for each method with success if the concept is simply detected in the image without any localisation
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).
The evaluation script can be found here.
Note: In order to participate in this challenge you have to sign an End User Agreement (EUA). You will find more information on the 'Resources' tab.
ImageCLEF lab is part of the Conference and Labs of the Evaluation Forum: CLEF 2021. CLEF 2021 consists of independent peer-reviewed workshops on a broad range of challenges in the fields of multilingual and multimodal information access evaluation, and a set of benchmarking activities carried in various labs designed to test different aspects of mono and cross-language Information retrieval systems. More details about the conference can be found here.
Submitting a working note with the full description of the methods used in each run is mandatory. Any run that could not be reproduced thanks to its description in the working notes might be removed from the official publication of the results. Working notes are published within CEUR-WS proceedings, resulting in an assignment of an individual DOI (URN) and an indexing by many bibliography systems including DBLP. According to the CEUR-WS policies, a light review of the working notes will be conducted by ImageCLEF organizing committee to ensure quality. As an illustration, ImageCLEF 2020 working notes (task overviews and participant working notes) can be found within CLEF 2020 CEUR-WS proceedings.
Participants of this challenge will automatically be registered at CLEF 2021. In order to be compliant with the CLEF registration requirements, please edit your profile by providing the following additional information:
Regarding the username, please choose a name that represents your team.
This information will not be publicly visible and will be exclusively used to contact you and to send the registration data to CLEF, which is the main organizer of all CLEF labs
Participating as an individual (non affiliated) researcher
We welcome individual researchers, i.e. not affiliated to any institution, to participate. We kindly ask you to provide us with a motivation letter containing the following information:
the presentation of your most relevant research activities related to the task/tasks
your motivation for participating in the task/tasks and how you want to exploit the results
a list of the most relevant 5 publications (if applicable)
the link to your personal webpage
The motivation letter should be directly concatenated to the End User Agreement document or sent as a PDF file to bionescu at imag dot pub dot ro. The request will be analyzed by the ImageCLEF organizing committee. We reserve the right to refuse any applicants whose experience in the field is too narrow, and would therefore most likely prevent them from being able to finish the task/tasks.
Information will be posted after the challenge ends.
ImageCLEF 2021 is an evaluation campaign that is being organized as part of the CLEF initiative labs. The campaign offers several research tasks that welcome participation from teams around the world. The results of the campaign appear in the working notes proceedings, published by CEUR Workshop Proceedings (CEUR-WS.org). Selected contributions among the participants, will be invited for publication in the following year in the Springer Lecture Notes in Computer Science (LNCS) together with the annual lab overviews.
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You can find additional information on the challenge here: https://www.imageclef.org/2021/coral