Graph Laplacian for Image Anomaly Detection
This work addresses image anomaly detection for applications like hyperspectral and medical imaging, offering an incremental improvement over existing methods.
The paper tackles image anomaly detection by proposing a graph-based method that overcomes limitations of the Reed-Xiaoli detector, achieving significant performance gains over state-of-the-art algorithms in tests on hyperspectral and medical images.
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.