Image Anomalies: a Review and Synthesis of Detection Methods

TL;DR

This paper reviews and synthesizes various image anomaly detection methods, classifying them by structural assumptions, and proposes universal, generic algorithms that improve false positive control and generalization.

Contribution

It introduces a unified framework for classifying anomaly detection methods based on structural assumptions and proposes generic algorithms with false positive control.

Findings

Universal detection thresholds improve false positive control.

Generic algorithms outperform specialized methods on benchmark datasets.

Anomaly detection can be effectively performed on a single image.

Abstract

We review the broad variety of methods that have been proposed for anomaly detection in images. Most methods found in the literature have in mind a particular application. Yet we show that the methods can be classified mainly by the structural assumption they make on the "normal" image. Five different structural assumptions emerge. Our analysis leads us to reformulate the best representative algorithms by attaching to them an a contrario detection that controls the number of false positives and thus derive universal detection thresholds. By combining the most general structural assumptions expressing the background's normality with the best proposed statistical detection tools, we end up proposing generic algorithms that seem to generalize or reconcile most methods. We compare the six best representatives of our proposed classes of algorithms on anomalous images taken from classic papers on the subject, and on a synthetic database. Our conclusion is that it is possible to perform automatic anomaly detection on a single image.