Image annotation refers to the process of labeling or tagging objects, regions, or specific features within an image. This is a key step in preparing data for machine learning tasks, particularly in supervised learning. The goal is to provide a model with labeled data so it can learn to recognize patterns or objects in unseen images. Common types of image annotation include: 1) Bounding Boxes, where a rectangle is drawn around an object of interest to highlight its location in the image. This is often used in object detection tasks. 2) Semantic Segmentation, where each pixel in the image is labeled with a class. This is useful in applications like autonomous driving, where the model needs to understand the boundaries of each object, such as roads, vehicles, and pedestrians. 3) Keypoint Annotation, where key facial features (e.g., eyes, nose, and mouth) or other points are marked for use in tasks like facial recognition or pose estimation. 4) Polygons, which involve drawing a shape around an object with more complex boundaries, typically used for more irregularly shaped objects in medical imaging or satellite image analysis. Annotation is essential for training machine learning models, especially in tasks like object detection, facial recognition, and segmentation. It can be done manually, using tools like LabelImg for bounding boxes, or with automated systems in more complex environments.
What is image annotation? What are its types?
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