Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
This addresses the challenge of clustering subtle anomalous patterns in images, which is incremental as it builds on existing clustering methods with a new distance function.
The paper tackles the problem of anomaly clustering, grouping images into clusters of anomaly types rather than just detecting anomalies, by proposing a framework using patch-based embeddings and a novel distance function. It achieves normalized mutual information scores of 0.451 and 0.674 on MVTec categories, improving to 0.577 and 0.669 with labeled data, significantly exceeding baselines and state-of-the-art methods.
We study anomaly clustering, grouping data into coherent clusters of anomaly types. This is different from anomaly detection that aims to divide anomalies from normal data. Unlike object-centered image clustering, anomaly clustering is particularly challenging as anomalous patterns are subtle and local. We present a simple yet effective clustering framework using a patch-based pretrained deep embeddings and off-the-shelf clustering methods. We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings. The weight defines the importance of instances (i.e., patch embeddings) in the bag, which may highlight defective regions. We compute weights in an unsupervised way or in a semi-supervised way when labeled normal data is available. Extensive experimental studies show the effectiveness of the proposed clustering framework along with a novel distance function upon exist-ing multiple instance or deep clustering frameworks. Over-all, our framework achieves 0.451 and 0.674 normalized mutual information scores on MVTec object and texture categories and further improve with a few labeled normal data (0.577, 0.669), far exceeding the baselines (0.244, 0.273) or state-of-the-art deep clustering methods (0.176, 0.277).