Image annotation is playing a significant role in computer vision, the technology that enables computers to gain high-level knowledge from digital images or videos and to see and understand visual information just like humans. Image annotation is the mechanism of labeling images of a dataset to train a machine learning model. Therefore, image annotation is used to label the characteristics you need your system to recognize. Training an ML model with labeled data is called Supervised Learning. The annotation task usually involves manual work, sometimes with computer-assisted help. A Machine Learning engineer establishes the labels, known as “categories”, and provides the image-specific information to the computer vision model. After the model is trained and brought into successful action, it will predict and recognize those established attributes in new images that have not been annotated yet. Image Labelling is necessary for special activity datasets because it lets the training model detect the great important parts of the image are, that is, categories so that it can later use those ideas to identify those categories in new, never-before-seen images. Depth Analysis on Image Annotation Types Image annotation is often used for image classification, object detection, object recognition, image segmentation, machine learning, and computer vision models. It is the performance used to create quality datasets for the models to train on and thus is useful for supervised and semi-supervised machine learning models. The purposes of image annotation (image categorization, object detection, etc.) require different executions of image annotation in order to develop successful datasets.
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