Graph-based Image Anomaly Detection

RX Detector 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 highly 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 on the Graph Fourier Transform, we are able to overcome some of RX Detector'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 RX Detector performance.