AnomalyHop: An SSL-based Image Anomaly Localization Method

Abstract

An image anomaly localization method based on the successive subspace learning (SSL) framework, called AnomalyHop, is proposed in this work. AnomalyHop consists of three modules: 1) feature extraction via successive subspace learning (SSL), 2) normality feature distributions modeling via Gaussian models, and 3) anomaly map generation and fusion. Comparing with state-of-the-art image anomaly localization methods based on deep neural networks (DNNs), AnomalyHop is mathematically transparent, easy to train, and fast in its inference speed. Besides, its area under the ROC curve (ROC-AUC) performance on the MVTec AD dataset is 95.9%, which is among the best of several benchmarking methods. Our codes are publicly available at Github.

arXiv comment5 pages, 3 figures

References in corpus (8)

• Sub-Image Anomaly Detection with Deep Pyramid Correspondences
• Explainable Deep One-Class Classification
• PointHop: An Explainable Machine Learning Method for Point Cloud Classification
• R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method
• Subspace Support Vector Data Description
• Multimodal Subspace Support Vector Data Description
• Successive Subspace Learning: An Overview
• NITES: A Non-Parametric Interpretable Texture Synthesis Method

Cited by in corpus (1)

• GSIP: Green Semantic Segmentation of Large-Scale Indoor Point Clouds