This is the application manual of BIIGLE. Here you can find articles on how to use the application as well as reference publications and the developer documentation.
Learn how you can manage your user account.
View and manage BIIGLE notifications in the notification center.
Learn what projects are and how to manage them.
Learn what label trees are and how you can manage them.
Learn how to create, modify or delete labels of a label tree.
Everything you need to know about versioned label trees.
View and resolve differences between label trees.
The annotation catalog shows you all annotations with a certain label.
Learn more on how you can download a label tree and use it elsewhere.
The volume overview allows you to explore all files that belong to a volume.
Annotation sessions can be used to conduct scientific studies.
Upload metadata to add information that can't be extracted from the files.
File labels are labels that are attached to whole images or videos.
Import annotations and file labels from metadata files.
Remote locations serve volume files from a public web server.
Storage disks serve volume files from a cloud storage service.
With storage requests you can upload volume files directly to BIIGLE.
A quick introduction to the image annotation tool.
Learn about all the tools that are available to create new image annotations.
Learn about all the tools to modify or delete existing image annotations.
Learn about advanced ways to navigate the images in the image annotation tool.
All sidebar tabs of the image annotation tool explained.
A list of all available shortcut keys in the image annotation tool.
Advanced configuration of the image annotation tool.
An introduction to the video annotation tool.
Learn how to create different kinds of video annotations.
Learn about the video timeline and how to navigate it.
Learn about all the tools to modify or delete existing video annotations.
All sidebar tabs of the video annotation tool explained.
A list of all available shortcut keys in the video annotation tool.
Advanced configuration of the video annotation tool.
The Label Review Grid Overview and what you can do with it.
A description of the file formats of the different available reports.
A detailed description of image location reports with a short introduction to QGIS.
A detailed description of the annotation position estimation of the annotation location report.
The image volume map shows the locations of images on a world map.
The automatic laser point detection is used to determine the visual footprint of images.
Learn how to ask any person for advice on the label of an image annotation.
An introduction to the Machine Learning Assisted Image Annotation method (MAIA).
Using novelty detection to obtain training data.
Using existing annotations to obtain training data.
Using knowledge transfer to obtain training data.
Reviewing the training proposals from novelty detection.
The automatic object detection.
Reviewing the annotation candidates from object detection.
Reference publications that you should cite if you use BIIGLE for one of your studies.
BIIGLE 2.0
Langenkämper, D., Zurowietz, M., Schoening, T., & Nattkemper, T. W. (2017). Biigle 2.0-browsing and annotating large marine image collections.
Frontiers in Marine Science, 4, 83. doi: 10.3389/fmars.2017.00083
Observations From Four Years of BIIGLE 2.0
Zurowietz, M., & Nattkemper, T. W. (2021). Current Trends and Future Directions of Large Scale Image and Video Annotation: Observations From Four Years of BIIGLE 2.0.
Frontiers in Marine Science, 8, 760036. doi: 10.3389/fmars.2021.760036
Video Object Tracking
Lukezic, A., Vojir, T., ˇCehovin Zajc, L., Matas, J., & Kristan, M. (2017). Discriminative correlation filter with channel and spatial reliability. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6309-6318). doi: 10.1109/CVPR.2017.515
Laser Point Detection
Schoening, T., Kuhn, T., Bergmann, M., & Nattkemper, T. W. (2015). DELPHI—fast and adaptive computational laser point detection and visual footprint quantification for arbitrary underwater image collections.
Frontiers in Marine Science, 2, 20. doi: 10.3389/fmars.2015.00020
MAIA
Zurowietz, M., Langenkämper, D., Hosking, B., Ruhl, H. A., & Nattkemper, T. W. (2018). MAIA—A machine learning assisted image annotation method for environmental monitoring and exploration.
PloS one, 13(11), e0207498. doi: 10.1371/journal.pone.0207498
UnKnoT
M. Zurowietz and T. W. Nattkemper, "Unsupervised Knowledge Transfer for Object Detection in Marine Environmental Monitoring and Exploration,"
in IEEE Access, vol. 8, pp. 143558-143568, 2020, doi: 10.1109/ACCESS.2020.3014441.
You may access most of the functionality of this application using the RESTful API. Most of the API requires user authentication via session cookie (being logged in to the website) but it is also available for external requests using a personal API token. You can manage your API tokens in the user settings.
API access is rate-limited to 10,800 requests per hour (3,600 for unauthenticated users). You may access the rate limit and the current number of remaining requests through the X-RateLimit-Limit and X-RateLimit-Remaining HTTP headers.
The API works with form (x-www-form-urlencoded) as well as JSON requests. For form requests, you can use method spoofing to use different HTTP methods. For the complete documentation, check out the link below.