[3D1-GS-2] Machine learning: Image recognition

[3D1-GS-2-01] Combining Feature Statistics and Clustering for Image Anomaly Detection

〇Hiroto Nakata¹, Katsuya Hotta², Kohei Nakazawa¹, Jun Yu³, Chao Zhang¹ (1. University of Fukui, 2. Iwate University, 3. Niigata University)

Keywords:Machine Learning, Deep Learning, Anomaly Detection

One-class classification based image anomaly detection aims to identify anomalies in images by using only normal images. Recent methods that use a memory bank to store deep feature representations of normal images have achieved high performance and are robust to data variation. However, to make the inference procedure efficient, feature reduction is usually conducted over the memory bank. Thus, statistical information may also be reduced. Therefore, we propose an anomaly detection method that combines feature statistics and clustering. Specifically, the anomalies are identified by a weighted anomaly score based on K-center clustering with feature reduction and Mahalanobis distance without feature reduction. As a result, the proposed computation of anomaly score incorporates both the idea of efficient nearest neighbor retrieval and the idea of out-of-distribution detection in the memory bank. In the experiments, we evaluate the anomaly detection performance at pixel level on the MVTec AD benchmark and show the superiority.