Unsupervised Image Outlier Detection using RANSAC

Image outlier detection (OD) is an essential tool to ensure the quality and accuracy of image datasets used in computer vision tasks. Most existing approaches, however, require a set of in-distribution data for training prior to outlier prediction. The quality and quantity of the data can influence the resulting performance. Thus, selecting a suitable in-distribution set often requires considerable effort. In this work, we propose RANSAC-NN, an unsupervised image OD algorithm designed to detect outliers within contaminated sets in a one-class classification fashion. Without any training, RANSAC-NN performs favorably in comparison to other well-established methods in a variety of OD benchmarks. Furthermore, we show that our method can enhance the robustness of existing OD methods by simply applying RANSAC-NN during pre-processing.