Learning-based Material Classification in X-ray Security Images

Emil Benedykciuk, Marcin Denkowski, Krzysztof Dmitruk

Abstract: Although a large number of papers have been published on material classification in the X-ray images, relatively few of them study X-ray security raw images as regards of material classification. This paper takes into consideration the task of materials classification into four main types of organics and metals in images obtained from Dual-Energy X-ray (DEXA) security scanner. We adopt well-known methods of machine learning and conduct experiments to examine the effects of various combinations of data and algorithms for generalization of the material classification problem. The methods giving the best results (Random Forests and Support Vector Machine) were used to predict the materials at every pixel in the testing image. The results motivate a novel segmentation scheme based on the multi-scale patch classification. This paper also introduces a new, open dataset of X-ray images (MDD) of various materials. The database contains over one million samples, labelled and stored in its raw