Image Anomalies: A Review and Synthesis of Detection Methods

• Published in: Journal of mathematical imaging and vision Vol. 61; no. 5; pp. 710 - 743
• Main Authors: Ehret, Thibaud, Davy, Axel, Morel, Jean-Michel, Delbracio, Mauricio
• Format: Journal Article
• Language: English
• Published: New York Springer US 15.06.2019; Springer Nature B.V
• Subjects: Algorithms; Anomalies; Applications of Mathematics; Computer Science; Image detection; Image Processing and Computer Vision; Mathematical Methods in Physics; Normality; Signal,Image and Speech Processing; Hypothesis testing; Sparsity; Center-surround; Background subtraction; Number of false alarms; Multi-scale; value; Anomaly detection; Background modeling; A-contrario assumption; Self-similarity
• ISSN: 0924-9907; 1573-7683
• DOI: 10.1007/s10851-019-00885-0

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 focus on a classification of the methods based on the structural assumption they make on the “normal” image, assumed to obey a “background model.” Five different structural assumptions emerge for the background model. Our analysis leads us to reformulate the best representative algorithms in each class by attaching to them an a-contrario detection that controls the number of false positives and thus deriving a uniform detection scheme for all. By combining the most general structural assumptions expressing the background’s normality with the proposed generic statistical detection tool, we end up proposing several 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 hints that it is possible to perform automatic anomaly detection on a single image.