Application of optimal clustering and metric learning to patch-based anomaly detection

•application of a clustering scheme for effcient anomaly detection.•to propose a way of adopting metric learning for optimal clustering.•to prove the effectiveness of the proposed approach by accuracy comparison.•to show potential extension to any patch-based detection. Anomaly detection in computer vision is a process of identifying observations or events deviated from the norm. Pixel- and patch-based approaches for locating abnormal regions suffer from a tremendously expensive computational complexity due to repeated calculations inherently required. The approach introduced in this letter aims to reducing such computational burden by employing an optimal clustering method for features extracted from a deep network-based encoder, and a metric learning which provides a way to bias the clusters found by the clustering algorithm. A set of loss functions is defined and used for training the encoder. After the training steps, the clustering result obtained from the training dataset is saved and used in the inference phase by measuring distances between the features of the input data and the clusters. The proposed approach has been applied to a patch-based anomaly detection, Patch-SVDD, to clarify the effectiveness of the idea. The experiment carried out with the implementation on MVTec-AD dataset results in improved detection speed by 10∼35% and better detection accuracy for many image classes, as well. In addition, the ablation study conducted proves the validity of the ideas introduced in the proposed approach.