Anomaly Detection in X-ray Security Imaging: a Tensor-Based Learning Approach

Anomaly detection in X-ray security screening systems has earned a lot of interests in recent years and has attracted many researchers working in the area of machine learning. With the advances in computing technology, it is becoming more feasible to develop an approach for automated anomaly detection in security screening systems based on images collected via Xray machines. Analyzing these X-ray images and constructing a detection model is considered as a challenging problem because of the lack or limited number of samples of anomalous objects. This paper presents a novel tensor based learning method for anomaly detection in X-ray security screening systems based on tensor analysis augmented with one-class classification model. Our method initially performs data fusion of multi-angle scanned images in a tensor data structure from where we extract the informative features. Further, it constructs a one-class support vector machine model using these features to detect anomalies. We evaluate this approach using two image-based datasets and one real X-ray security baggage data collected from Sydney airport. The results show that our tensor based learning method outperforms other state-of-the-art approaches.