A Semantic-Enhanced Method Based On Deep SVDD for Pixel-Wise Anomaly Detection

Abstract

Detecting the anomalous information in multimedia is valuable to many computer vision applications. Recently, many pixel-wise methods modeling by deep learning model have been presented, which can be divided in reconstruction-based and distance-based methods. However, reconstruction-based methods suffer from the low precision of pixel reconstructions. Distance-based methods extract the hierarchical features by a pre-trained model, in order to estimate the anomalies by distances between normal and anomalous features. Nevertheless, multi-level features are ignored in these methods, and semantic information is not considered which is important to enhance the description of anomalies. To over-come the problems, we propose a novel semantic-enhanced anomaly detection method based on deep Support Vector Data Description (SVDD). A new semantic correlation module (SCB) is introduced to enhance the semantic information of the feature representations by cosine similarity. Mean-while, the multi-level architecture is utilized to estimate the final pixel-wise anomaly score. Experimental results demonstrate the proposed method outperforms state-of-the-art methods on MVTec and STC dataset.