Image annotation is a human-driven task of automatically labeling an image with tags. The tags are pre-determined by the AI expert and are chosen to provide sufficient information for the computer vision system to interpret what is being presented in the image. Depending on the application, the number of tags per image can differ widely.
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There are various uses for image annotation in computer vision tasks. One popular use is to provide higher quality labels to visual elements in the images, such as faces in image search. This is because human eyes are much better able to detect facial features than binoculars or a computer vision camera. Another use for image annotation is to provide extra information to computer vision experts during image segmentation or face detection in medical imaging.
Humans can also use image annotation services for non-image-related tasks like document analysis. For example, documents with structured texts are best edited with proper sentence labels that highlight the most important verbs or ideas in each sentence. This allows the human editor to concentrate on the essential parts of the document without spending too much time on unimportant tags or keywords. The process is also easily applicable to audio files with metadata. As such, document databases and other Machine Learning systems will benefit from the image data, and the resulting classifiers can generate more accurate results for a given input.
Image labels can also be generated using machine learning tasks. There is quite a bit of work in building a label generator from scratch that can recognize handwritten numbers, handwritten symbols, and handwritten dates and times. However, even this comparatively simple task can be made much more robust by combining various tools of image annotation with traditional Machine Learning techniques.
A human expert can also be used in generating image labels; however, this will only be practical if the labeled images are already labeled with a Language Identification tag. In situations where there is no such pre-requisite, expert human intervention will increase the accuracy of the labeling task significantly. Image segmentation deep learning models are already in use to segment images into different regions and to label them according to their dimensions. This method is also applied in Natural Language Processing.