Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection
Tetiana Gula João P C Bertoldo
Anomaly detection (AD) in images, identifying significant deviations from
normality, is a critical issue in computer vision. This paper introduces a
novel approach to dimensionality reduction for AD using pre-trained
convolutional neural network (CNN) that incorporate EfficientNet models. We
investigate the importance of component selection and propose two types of tree
search approaches, both employing a greedy strategy, for optimal eigencomponent
selection. Our study conducts three main experiments to evaluate the
effectiveness of our approach. The first experiment explores the influence of
test set performance on component choice, the second experiment examines the
performance when we train on one anomaly type and evaluate on all other types,
and the third experiment investigates the impact of using a minimum number of
images for training and selecting them based on anomaly types. Our approach
aims to find the optimal subset of components that deliver the highest
performance score, instead of focusing solely on the proportion of variance
explained by each component and also understand the components behaviour in
different settings. Our results indicate that the proposed method surpasses
both Principal Component Analysis (PCA) and Negated Principal Component
Analysis (NPCA) in terms of detection accuracy, even when using fewer
components. Thus, our approach provides a promising alternative to conventional
dimensionality reduction techniques in AD, and holds potential to enhance the
efficiency and effectiveness of AD systems.