k-NNN: Nearest Neighbors of Neighbors for Anomaly Detection

Anomaly detection aims at identifying images that deviate significantly from the norm. We focus on algorithms that embed the normal training examples in space and, when given a test image, detect anomalies based on the features' distance to the k-nearest training neighbors. We propose a new operator that takes into account the varying structure & importance of the features in the embedding space. Interestingly, this is achieved by considering not only the nearest neighbors but also the neighbors of these neighbors (k-NNN). Our results demonstrate that by simply replacing the nearest neighbor component in existing algorithms with our k-NNN, while leaving the rest of the algorithms unchanged, the performance of each algorithm is improved. This holds true for both common homogeneous datasets, such as specific flowers, as well as for more diverse datasets.