Graph Laplacian for Image Anomaly Detection
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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2020-02-07
Major/Subject
Mcode
Degree programme
Language
en
Pages
16
Series
MACHINE VISION AND APPLICATIONS, Volume 31, issue 1
Abstract
Reed–Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD’s limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state of the art.Description
Keywords
Anomaly detection, Graph Fourier transform, Graph-based image processing, Principal component analysis, Hyperspectral images, PET, TARGET DETECTION, RX-ALGORITHM, CLASSIFICATION, SEGMENTATION, REPRESENTATION, PERFORMANCE, MODELS, NOISE
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Citation
Verdoja, F & Grangetto, M 2020, ' Graph Laplacian for Image Anomaly Detection ', MACHINE VISION AND APPLICATIONS, vol. 31, no. 1, 11 . https://doi.org/10.1007/s00138-020-01059-4
