Learning symbolic descriptions of shape for object recognition in X-ray images

Abstract

In this paper we describe a method for learning shape descriptions of objects in X-ray images. The descriptions are induced from shape examples using the AQ15c inductive learning system. The method has been experimentally compared to k-nearest neighbor, a statistical pattern recognition technique, the C4.5 decision tree learning program, and a multilayer feed-forward neural network. Experimental results demonstrate strong advantages of the AQ methodology over the other methods. Specifically, the method has higher predictive accuracy and faster learning and recognition rates. AQ's representation language, VL 1, was better suited for this problem, which can be seen by examining the empirical results and the learned rules. The method was applied to the problem of detecting blasting caps in X-ray images of luggage. An intelligent system performing this detection task can be used to assist airport security personnel with luggage screening.