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Image novelty detection based on mean-shift and typical set size

  • The detection of anomalous or novel images given a training dataset of only clean reference data (inliers) is an important task in computer vision. We propose a new shallow approach that represents both inlier and outlier images as ensembles of patches, which allows us to effectively detect novelties as mean shifts between reference data and outliers with the Hotelling T2 test. Since mean-shift can only be detected when the outlier ensemble is sufficiently separate from the typical set of the inlier distribution, this typical set acts as a blind spot for novelty detection. We therefore minimize its estimated size as our selection rule for critical hyperparameters, such as, e.g., the size of the patches is crucial. To showcase the capabilities of our approach, we compare results with classical and deep learning methods on the popular datasets MNIST and CIFAR-10, and demonstrate its real-world applicability in a large-scale industrial inspection scenario.

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Metadaten
Author:Matthias Hermann, Bastian Goldlücke, Matthias O. FranzORCiDGND
DOI:https://doi.org/10.1007/978-3-031-06430-2_63
ISBN:978-3-031-06429-6
ISBN:978-3-031-06430-2
Parent Title (English):Image Analysis and Processing - ICIAP 2022, 21th International Conference, May 23 - 27 May 2022, Lecce, Italy, hybrid, Proceedings, Part I, (Lecture Notes in Computer Science, Volume 13231)
Publisher:Springer
Place of publication:Cham
Document Type:Conference Proceeding
Language:English
Year of Publication:2022
Release Date:2022/12/07
Tag:Image novelty detection; Independent component analysis; Mean-shift
First Page:755
Last Page:766
Note:
Corresponding author: Matthias Hermann
Relevance:Peer reviewed nach anderen Listungen (mit Nachweis zum Peer Review Verfahren)
Open Access?:Nein
Licence (German):License LogoUrheberrechtlich geschützt