Papers › Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature
Qian Wan, Liang Gao, Xinyu Li, long wen
Anomaly localization is valuable for improvement of complex production processing in smart manufacturing system. As the distribution of anomalies is unknowable and labeled data is few, unsupervised methods based on convolutional neural network (CNN) have been studied for anomaly localization. But there are still problems for real industrial applications, in terms of localization accuracy, computation time, and memory storage. This article proposes a novel framework called as Gaussian clustering of pretrained feature (GCPF), including the clustering and inference stage, for anomaly localization in unsupervised way. The GCPF consists of three modules which include pretrained deep feature extraction (PDFE), multiple independent multivariate Gaussian clustering (MIMGC), and multihierarchical anomaly scoring (MHAS). In the clustering stage, features of normal images are extracted by pretrained CNN at the PDFE module, and then clustered at the MIMGC module. In the inference stage, features of target images are extracted and then scored for anomaly localization at the MHAS module. The GCPF is compared with the state-of-the-art methods on MVTec dataset, achieving receiver operating characteristic curve of 96.86% over all 15 categories, and extended to NanoTWICE and DAGM datasets. The GCPF outperforms the compared methods for unsupervised anomaly localization, and significantly reserves the low computation complexity and online memory storage which are important for real industrial applications.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | MVTec AD | GCPF | Detection AUROC | 93.1 | #102 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | GCPF | Segmentation AUROC | 96.86 | #102 of 148 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections