Graph Laplacian for Image Anomaly Detection

Francesco Verdoja, Marco Grangetto

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
35 Downloads (Pure)

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.
Original languageEnglish
Article number11
Number of pages16
JournalMACHINE VISION AND APPLICATIONS
Volume31
Issue number1
DOIs
Publication statusPublished - 7 Feb 2020
MoE publication typeA1 Journal article-refereed

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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