Definition
Image annotation is the process of labeling objects, regions, or attributes in images to create datasets for computer vision models. Annotations may be bounding boxes, polygons, or segmentation masks.
Purpose
The purpose is to provide training data that helps AI recognize objects, scenes, or patterns in visual data.
Importance
- Critical for supervised computer vision tasks.
- Quality affects model performance directly.
- Labor-intensive and may require domain expertise.
- Used in diverse fields from medicine to autonomous vehicles.
How It Works
- Collect raw images from cameras or datasets.
- Define annotation schema (e.g., objects, categories).
- Annotators label images using tools.
- Validate with audits for accuracy.
- Export annotated data for training.
Examples (Real World)
- COCO Dataset: annotated with bounding boxes and segmentation.
- Tesla: annotates driving scenes for autonomous vehicle training.
- Labelbox: platform providing large-scale image annotation services.
References / Further Reading
- COCO Dataset — cocodataset.org.
- Pascal VOC Challenge — University of Oxford.
- Data Annotation for AI — NIST.