Image annotation refers to the process of marking an image with metadata or supplementary information related to its content. This may include adding text labels or tags to describe the objects, individuals, or scenes shown in the image, as well as drawing bounding boxes or other shapes around particular objects or areas of interest.
In the realm of computer vision, image annotation is a common practice often employed to generate training and validation datasets for machine - learning algorithms. For example, when developing a machine-learning model to classify animal pictures, the images in the training dataset need to be labeled with terms such as "cat", "dog", or "bird". Subsequently, the model is trained on this dataset, and its performance is evaluated based on its ability to accurately classify new, unseen images.
There are several approaches available for image annotation, including manual annotation, semi-automatic annotation, and fully-automated annotation. Human annotation, which involves meticulously examining and identifying each image in a dataset, can yield the most precise and dependable annotations. Fully-automated annotation uses algorithms to generate annotations automatically, while semi-automatic annotation utilizes tools to accelerate the manual annotation process.
Currently, mainstream image annotation platforms include Roboflow, Labelbox, Label Studio, T-Rex Label.