Patch Feature Transformation: An Anomaly Detection Method with Succinct Feature Filtering

Anomaly detection is often approached as an out-of-distribution (OOD) detection task, where a feature distribution from normal samples is constructed, and deviations are flagged as anomalies. This approach is dependent on manual labeling, as subtle visual anomalies can be easily overlooked, resulting in the potential for bias in labeling and subsequent unsatisfactory detection results. Based on the issue, we propose Anomaly Detection with Succinct Feature Filtering (ADSFF) for unlabeled samples. Our method avoids sample labeling bias and provides a solution to the coexistence of anomalous and normal features in the feature space of unlabeled samples. ADSFF includes a data preprocessing module and a feature filtering module, where the data preprocessing module improves the visibility of subtle anomalies, while the feature filtering module screens the local features of the samples. In feature filtering, we found that feedforward neural networks do not lose feature information during the feature transformation process. Consequently, we utilized feedforward neural networks for feature filtering and achieved expected results. Furthermore, we investigate the impact of sample imbalance on the task of anomaly detection using unlabeled samples. This paper assesses the performance of ADSFF using the MVTec AD and BeanTech Anomaly Detection (BTAD) datasets. The results demonstrate that ADSFF achieves an average area under the curve (AUC) of 0.978 on the MVTec AD and an average AUC of 0.942 on the BTAD. ADSFF outperformed other methods on seven test datasets in MVTec AD, achieving the highest average accuracy on MVTec AD.