Multiscale anomaly detection using diffusion maps and saliency score

Recently, we presented a multiscale approach to anomaly detection in images, combining diffusion maps for dimensionality reduction and a nearest-neighbor-based anomaly score in the reduced dimension. When applying diffusion maps to images, usually a process of sampling and out-of-sample extension is used, which has limitations in regards to anomaly detection. To overcome the limitations, a multiscale approach was proposed, which drives the sampling process to ensure separability of the anomaly from the background clutter. In this paper, we propose a new anomaly score used in the diffusion map space, which shows increased performance. We show that this algorithm enables improved detection when tested on side-scan sonar images of sea-mines and compare it with competing algorithms.