Image segmentation optimisation for X-ray images of airline luggage

Airline luggage contains a wide variety of objects and their automated image analysis require good quality image segmentation. Given the fact that such images are highly cluttered, it is non trivial task to optimise image segmentation algorithms. In this paper we present a methodology for optimising image segmentation algorithms based on image properties without manual intervention. The methodology computes image properties such as average edge gradient strength, inter- vs. intra-cluster distances using image colour features, and colour purity of resultant regions, to train a neural network that maps these to ground-truth labelling on the acceptability (good or bad) of the solution (resultant segmentation). We show that on unseen test data, this methodology performs extremely well by correctly predicting the optimal parameters of image segmentation algorithms used.