Locally Varying Distance Transform for Unsupervised Visual Anomaly Detection

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Locally Varying Distance Transform for Unsupervised Visual Anomaly Detection
Title:
Locally Varying Distance Transform for Unsupervised Visual Anomaly Detection
Journal Title:
Lecture Notes in Computer Science
Keywords:
Publication Date:
02 November 2022
Citation:
Lin, W.-Y., Liu, Z., & Liu, S. (2022). Locally Varying Distance Transform for Unsupervised Visual Anomaly Detection. Computer Vision – ECCV 2022, 354–371. https://doi.org/10.1007/978-3-031-20056-4_21
Abstract:
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 rely on global transformations that truncate minor variations. As anomalies are rare, the final embedding often lacks the key variations needed to distinguish anomalies from normal instances. This paper proposes a new embedding using a set of locally varying data projections, with each projection responsible for persevering the variations that distinguish a local cluster of instances from all other instances. The locally varying embedding ensures the variations that distinguish anomalies are preserved, while simultaneously allowing the probability that an instance belongs to a cluster, to be statistically inferred from the one-dimensional, local projection associated with the cluster. Statistical agglomeration of an instance’s cluster membership probabilities, creates a global measure of its affinity to the dataset and causes anomalies to emerge, as instances whose affinity scores are surprisingly low.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: I2R
Grant Reference no. : SC20/19-72867G-N150

This research is supported by core funding from: I2R
Grant Reference no. : SC20/19-128610-CORE
Description:
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-20056-4_21
ISSN:
9783031200564
ISBN:
9783031200557
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