Domain anomaly detection in machine perception: a system architecture and taxonomy
Article
Kittler, J., Christmas, W., De Campos, T., Windridge, D., Yan, F., Illingworth, J. and Osman, M. 2014. Domain anomaly detection in machine perception: a system architecture and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36 (5), pp. 845-859. https://doi.org/10.1109/TPAMI.2013.209
Type | Article |
---|---|
Title | Domain anomaly detection in machine perception: a system architecture and taxonomy |
Authors | Kittler, J., Christmas, W., De Campos, T., Windridge, D., Yan, F., Illingworth, J. and Osman, M. |
Abstract | We address the problem of anomaly detection in machine perception. The concept of domain anomaly is introduced as distinct from the conventional notion of anomaly used in the literature. We propose a unified framework for anomaly detection which exposes the multifaceted nature of anomalies and suggest effective mechanisms for identifying and distinguishing each facet as instruments for domain anomaly detection. The framework draws on the Bayesian probabilistic reasoning apparatus which clearly defines concepts such as outlier, noise, distribution drift, novelty detection (object, object primitive), rare events, and unexpected events. Based on these concepts we provide a taxonomy of domain anomaly events. One of the mechanisms helping to pinpoint the nature of anomaly is based on detecting incongruence between contextual and noncontextual sensor(y) data interpretation. The proposed methodology has wide applicability. It underpins in a unified way the anomaly detection applications found in the literature. To illustrate some of its distinguishing features, in here the domain anomaly detection methodology is applied to the problem of anomaly detection for a video annotation system. |
Keywords | inference mechanisms; object detection; video signal processing; Bayesian probabilistic reasoning apparatus; contextual sensor data interpretation; domain anomaly concept; domain anomaly detection; machine perception; noncontextual sensor data interpretation; video annotation system; Bayes methods; Cognition; Computational modeling; Context; Data models; Detectors; Probabilistic logic; Domain anomaly; anomaly detection framework; anomaly detection mechanisms; machine perception |
Publisher | IEEE |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
ISSN | 0162-8828 |
Electronic | 1939-3539 |
Publication dates | |
May 2014 | |
Publication process dates | |
Accepted | 15 Dec 2013 |
Deposited | 27 Apr 2015 |
Output status | Published |
Accepted author manuscript | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TPAMI.2013.209 |
PubMed ID | 26353221 |
Language | English |
https://repository.mdx.ac.uk/item/851w1
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