As was mentioned above, data annotation can be manual or automatic.
People use special programs to annotate manually. They delineate objects' borders (in the case of images) and create textual notes. This process is very time-consuming. But at the same time, as long as the specialists are knowledgeable and precise, the output data will be high-quality and accurate.
Automatic annotation is a process when a computer assigns metadata (signatures or tags) to a digital image automatically using appropriate keywords to describe the image's visual content.
Existing algorithms can be divided into two categories:
- model-based learning methods which investigate the correlation between visual characteristics and their semantic meaning using machine learning or knowledge representation models;
- database-based models, which immediately produce a sequence of probable labels in accordance with the already annotated images in the existing database.
Labeling tools based on neural networks allow us to select objects much faster and more efficiently, in order to process a much larger number of images and to automate the bulk of manual labeling tasks, and they can be additionally trained to more accurately recognize new images.