Image annotation helps to make images readable for computer vision. Annotated images are useful for performance calculation of other fully automatic algorithm results. They are called as benchmark, ground truth or reference data. Comparing to the annotated images, it is possible to calculate true positives and false alarms of a fully automatic algorithm.
Annotation in machine learning is the process of labeling data, which could be in the form of text, images, audio, etc. In machine learning, computers can use the annotated data to learn to recognize similar patterns when presented with new data. Annotation is typically done manually by humans, but crowdsourcing can speed up the process and spread out the workload.There are many marking techniques for image annotation that are used conventionally.
Bounding boxes are an important method of image anno...