Graph Laplacian for Image Anomaly Detection
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.
Code (1)
Tasks
Anomaly DetectionSimilar Papers 제목 키워드 기반
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization
Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anomaly detection is typically solved by measuring feature similarity of a …
Unsupervised Anomaly DetectionLaplacian Change Point Detection for Dynamic Graphs
Dynamic and temporal graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for many real world application…
Anomaly DetectionChange Point DetectionCommunity-Level Anomaly Detection for Anti-Money Laundering
Anomaly detection in networks often boils down to identifying an underlying graph structure on which the abnormal occurrence rests on. Financial fraud schemes are one such example, where more or less intricate schemes ar…
Anomaly DetectionDictionary LearningSpecificityLaplacian Change Point Detection for Single and Multi-view Dynamic Graphs
Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for many real world applications such as int…
Anomaly DetectionChange Point DetectionHLSAD: Hodge Laplacian-based Simplicial Anomaly Detection
In this paper, we propose HLSAD, a novel method for detecting anomalies in time-evolving simplicial complexes. While traditional graph anomaly detection techniques have been extensively studied, they often fail to captur…
Anomaly DetectionComputational EfficiencyGraph Anomaly Detection