Locally varying distance transform for unsupervised visual anomaly detection

• Main Authors: LIN, Wen-yan, LIU, Zhonghang, LIU, Siying
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: anomaly detection; unsupervised; high dimensions; Bayesian; Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/7310; https://ink.library.smu.edu.sg/context/sis_research/article/8313/viewcontent/1673.pdf
• Institution: Singapore Management University

Unsupervised anomaly detection on image data is notoriously unstable. We believe this is because many classical anomaly detectors implicitly assume data is low dimensional. However, image data is always high dimensional. Images can be projected to a low dimensional embedding but such projections rel...