A graph model-based multiscale feature fitting method for unsupervised anomaly detection

•A graph model-based multiscale feature fitting method is proposed for unsupervised anomaly detection to detect and localize anomalies.•The graph model between the query set and the gallery set is established according to the K nearest neighbor method.•The feature fitting representation of each vertex is calculated based on its KNN features according to the message flow in the graph model.•The idea of weighted multiscale anomaly map matching is proposed to obtain the anomaly map of each test image.