25,918 research outputs found
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.Comment: Published in Machine Vision and Applications (Springer
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN
Recently, the introduction of the generative adversarial network (GAN) and
its variants has enabled the generation of realistic synthetic samples, which
has been used for enlarging training sets. Previous work primarily focused on
data augmentation for semi-supervised and supervised tasks. In this paper, we
instead focus on unsupervised anomaly detection and propose a novel generative
data augmentation framework optimized for this task. In particular, we propose
to oversample infrequent normal samples - normal samples that occur with small
probability, e.g., rare normal events. We show that these samples are
responsible for false positives in anomaly detection. However, oversampling of
infrequent normal samples is challenging for real-world high-dimensional data
with multimodal distributions. To address this challenge, we propose to use a
GAN variant known as the adversarial autoencoder (AAE) to transform the
high-dimensional multimodal data distributions into low-dimensional unimodal
latent distributions with well-defined tail probability. Then, we
systematically oversample at the `edge' of the latent distributions to increase
the density of infrequent normal samples. We show that our oversampling
pipeline is a unified one: it is generally applicable to datasets with
different complex data distributions. To the best of our knowledge, our method
is the first data augmentation technique focused on improving performance in
unsupervised anomaly detection. We validate our method by demonstrating
consistent improvements across several real-world datasets.Comment: Published as a conference paper at ICDM 2018 (IEEE International
Conference on Data Mining
Contextual anomaly detection in crowded surveillance scenes
AbstractThis work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour
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