Semantic Metadata for Image Files

Abstract

Deep neural networks used in industry applications usually work the best when they are trained using supervised learning given that there is a lot of data available, that the data is from the same distribution as the data from the production environment and given that the labels are of a high quality. Large amounts of image data is available on the Internet, but it is not useful for machine learning applications in raw format. This concept goes through the process of image data acquisition from public sources on the Internet and automatic data labeling using existing computer vision deep learning models for object detection and instance segmentation. It sets up baseline labels that way and touches the problem of versioning labels from different sources for the same image and the same task. Eventually, it assigns machine learning semantic metadata to the given image. Furthermore, it goes beyond baseline automatic labels and proposes concept to refine them in both automatic and manual ways. With the semantic metadata available, it is shown how to make use of that semantic metadata to synthetically create even more image data and labels that deep learning depends on.