The authors present a method that can propose both images and areas within an image likely to contain desired classes.
Image collections, if critical aspects of image content are exposed, can spur research and practical applications in many domains. Supervised machine learning may be the only feasible way to annotate very large collections. However, leading approaches rely on large samples of completely and accurately annotated images. In the case of a large forensic collection that the authors are aiming to annotate, neither the complete annotation nor the large training samples can be feasibly produced. They, therefore, investigate ways to assist manual annotation efforts done by forensic experts. They present a method that can propose both images and areas within an image likely to contain desired classes. Evaluation of the method with human annotators showed highly accurate classification and reasonable segmentation accuracy that was strongly affected by transfer learning. The authors hope this effort can be helpful in other domains that require weak segmentation and have limited availability of qualified annotators. (Published abstract provided)