RANSAC-NN: Unsupervised Image Outlier Detection using RANSAC
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Image outlier detection (OD) is crucial for ensuring the quality and accuracy of image datasets used in computer vision tasks. The majority of OD algorithms, however, have not been targeted toward image data. Consequently, the results of applying such algorithms to images are often suboptimal. In this work, we propose RANSAC-NN, a novel unsupervised OD algorithm specifically designed for images. By comparing images in a RANSAC-based approach, our algorithm automatically predicts the outlier score of each image without additional training or label information. We evaluate RANSAC-NN against state-of-the-art OD algorithms on 15 diverse datasets. Without any hyperparameter tuning, RANSAC-NN consistently performs favorably in contrast to other algorithms in almost every dataset category. Furthermore, we provide a detailed analysis to understand each RANSAC-NN component, and we demonstrate its potential applications in image mislabeled detection. Code for RANSAC-NN is provided at https://github.com/mxtsai/ransac-nn
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