Mining Small Objects in Large Images Using Neural Networks

Since the late 1980s, neural networks have been widely applied to data mining. However, they are often criticised and regarded as a “black box” due to the lack of interpretation ability. This chapter describes a domain independent approach to the use of neural networks for mining multiple class, small objects in large images. In this approach, the networks are trained by the back propagation algorithm on examples which have been cut out from the large images. The trained networks are then applied, in a moving window fashion, over the large images to mine the objects of interest. During the mining process, both the classes and locations of the objects are determined. The network behaviour is interpreted by analysing the weights in learned networks. Visualisation of these weights not only gives an intuitive way of representing hidden patterns encoded in learned neural networks for object mining problems, but also shows that neural networks are not just a black box but an expression or a model of hidden patterns discovered in the data mining process.