Label

Labels in computer vision refer to textual or numerical annotations assigned to objects or regions of interest in an image or video. Labels are often used in supervised machine learning applications to train algorithms to recognize and classify objects in visual data. They can be used to identify objects, define their boundaries, or describe their properties, such as color, shape, or texture. Labels are often manually assigned by human annotators or automatically generated using computer vision algorithms. The quality and accuracy of labels can have a significant impact on the performance of computer vision systems.

Understanding label quality

The quality of labels in computer vision refers to the accuracy and consistency of annotations applied to visual data. High-quality labels are critical for training accurate machine learning models that can identify and classify objects and features in images. The quality of labels can be affected by a variety of factors, such as the expertise and experience of the annotator, the quality of the annotation tools used, and the complexity and ambiguity of the objects being labeled. To ensure high-quality labels, it is important to have clear labeling guidelines, standards, and processes in place, and to perform quality control checks and validation on the labels. This helps ensure that labels are consistent, accurate, and reliable, which is critical to the success of computer vision applications. Label quality can also be improved by using automated labeling tools with human involvement. 

Automatic labeling

Automatic labeling, also known as automatic annotation, is a process in computer vision that uses machine learning algorithms to apply labels to visual data, such as images or videos. Automatic labeling can be used to reduce the time and cost required for manual labeling and is particularly useful for large datasets. There are multiple techniques for automatic labeling, including object detection, semantic segmentation, and instance segmentation, which involve identifying and classifying objects in an image and labeling them accordingly. While automatic labeling can be effective, it can also be less accurate than manual labeling, especially when the visual data is complex or ambiguous. Therefore, a combination of automatic and manual labeling is often used to ensure the highest quality of labels for training machine learning models.

References

【1】https://encord.com/glossary/label-definition/