Encord Computer Vision Glossary
What is annotation for computer vision?
In AI, the process of adding labels or tags to a dataset to classify and categorize the data is referred to as data annotation. Machine learning algorithms, which are used to forecast the future or make decisions based on data, are often trained and enhanced through this process.
Because it helps verify that the data is appropriately represented and can be used by the algorithm, data annotation is a crucial stage in machine learning. Without accurate annotation, the algorithm might not be able to learn from the data correctly and might come to the wrong conclusion.
Several different types of annotation can be used in AI, including manual annotation, which involves human experts manually labeling the data, and automatic annotation, which uses algorithms to classify and categorize the data.
Manual annotation is often used when the data is complex or when it is not possible to classify the data using automated methods accurately. This can be a time-consuming process, but ensuring that the data is accurately labeled is often necessary.
What is automatic annotation for computer vision?
Automatic annotation, on the other hand, is often used when the data is simple and can be accurately classified using algorithms. This process can be faster, but it may not be as accurate as a manual annotation.
Other techniques, such as active learning, which involves using human feedback to improve the algorithm's predictions, and semi-supervised learning, which combines labeled and unlabeled data to improve the accuracy of the algorithm, can be used to increase the accuracy of machine learning algorithms in addition to manual and automatic annotation.
To ensure that the data used to train algorithms is appropriately labeled and classified, which is required to increase the accuracy and efficacy of the algorithm, annotation is a crucial aspect of the machine learning process.
Annotation Methods in Computer Vision
Here are some of the different annotation methods commonly used in computer vision:
Bounding box annotation
Bounding box annotation involves drawing a rectangle around an object in an image or video. The rectangle is used to indicate the object's location and size. Bounding box annotation is commonly used for object detection and localization tasks.
Polygon annotation involves drawing a series of connected straight lines to create a closed shape around an object in an image or video. Polygon annotation is used for objects with complex shapes and outlines.
Polyline annotation involves drawing a series of connected straight lines without closing the shape around an object in an image or video. Polyline annotation is used for objects with non-closed shapes, such as roads, rivers, or wires.
Keypoint annotation involves marking a single point on an object in an image or video. Point annotation is used for objects with specific features or landmarks, such as the eyes or nose on a face.
Segmentation mask annotation involves creating a mask that covers the entire object in an image or video. The mask is used to indicate the object's shape and location, with each pixel assigned a corresponding class label. Segmentation mask annotation is commonly used for object recognition and classification tasks.
Frame classification involves labeling entire frames in an image or video using radio buttons, checklists, or free-form text input. Frame classification is used for tasks where it is necessary to classify the entire frame, such as identifying the context of a scene.
Dynamic classification involves labeling objects in a video in real-time using radio buttons, checklists, or free-form text input. Dynamic classification is used for tasks where it is necessary to track objects in a video and update their annotations in real-time.
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