Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types

• URL: http://arxiv.org/abs/2112.11573v1
• Date: Tue, 21 Dec 2021 23:11:33 GMT
• Title: Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
• Authors: Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li, Chen-Yu Lee, Tomas Pfister
• Abstract summary: We introduce anomaly clustering, whose goal is to group data into coherent clusters of anomaly types. This is different from anomaly detection, whose goal is to divide anomalies from normal data. We present a simple yet effective clustering framework using a patch-based pretrained deep embeddings and off-the-shelf clustering methods.
• Score: 60.45942774425782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce anomaly clustering, whose goal is to group data into semantically coherent clusters of anomaly types. This is different from anomaly detection, whose goal is to divide anomalies from normal data. Unlike object-centered image clustering applications, 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 if labeled normal data is available. Extensive experimental studies show the effectiveness of the proposed clustering framework along with a novel distance function upon existing multiple instance or deep clustering frameworks. Overall, 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).

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