Review of Image Data Augmentation in Computer Vision

Abstract

Deep learning is a promising solution for computer vision at present. To solve the computer vision problem, it requires massive and high-quality image training datasets. Collecting and accurately labeling image datasets is a very time-consuming and expensive process. As computer vision applications become more widespread, it makes this problem even more pronounced. Image augmentation technologies are technical methods to effectively solve the problem of deep learning training under the condition of small-scale or low-quality training data. These technologies are continually accompanied with the development of deep learning and computer vision. This paper first reviews these image augmentation researches from the perspective of augmentation objects, operation spaces, label processing methods, and augmentation strategies and then concludes corresponding paradigms of current image data augmentation methods. After that, this paper proposes a taxonomy for current image data augmentation guided by the above paradigms, and reviews corresponding representative methods of each image data augmentation category. Finally, this paper makes conclusions on existing image data augmentation, points out the problems existing in the current image augmentation research and presents promising directions for future research.

Keywords

• deep learning
• computer vision
• image augmentation
• data augmentation
• image enhancement