  The screening of baggage using X-ray scanners is now routine in aviation
security with automatic threat detection approaches, based on 3D X-ray computed
tomography (CT) images, known as Automatic Threat Recognition (ATR) within the
aviation security industry. These current strategies use pre-defined threat
material signatures in contrast to adaptability towards new and emerging threat
signatures. To address this issue, the concept of adaptive automatic threat
recognition (AATR) was proposed in previous work. In this paper, we present a
solution to AATR based on such X-ray CT baggage scan imagery. This aims to
address the issues of rapidly evolving threat signatures within the screening
requirements. Ideally, the detection algorithms deployed within the security
scanners should be readily adaptable to different situations with varying
requirements of threat characteristics (e.g., threat material, physical
properties of objects). We tackle this issue using a novel adaptive machine
learning methodology with our solution consisting of a multi-scale 3D CT image
segmentation algorithm, a multi-class support vector machine (SVM) classifier
for object material recognition and a strategy to enable the adaptability of
our approach. Experiments are conducted on both open and sequestered 3D CT
baggage image datasets specifically collected for the AATR study. Our proposed
approach performs well on both recognition and adaptation. Overall our approach
can achieve the probability of detection around 90% with a probability of false
alarm below 20%. Our AATR shows the capabilities of adapting to varying types
of materials, even the unknown materials which are not available in the
training data, adapting to varying required probability of detection and
adapting to varying scales of the threat object.
