Ancient coins are difficult to classify as they exhibit a large amount of variation due to centering issues, differences in shape, colour and orientation, varying depictions of semantically identical elements, and patination or wear on the surface of the coin.
Most current identification methods are local feature based, but the loss of spatial relationships often results in bad performance. To overcome this limitation, approaches which divide a coin into segments have been tried, but these assume that coins have perfect centring, are registered accurately, and are nearly circular in shape, none of which are reliable assumptions.
All current work in ancient coin recognition relies on matching existing coin types. This limits the application of existing classification systems, as assuming that the unknown coin type is present in the training set is unrealistic – there are hundreds of thousands of different types of coin. Because of this, it is more useful to be able to describe what is depicted upon the coin, rather than classify it by type.
This study trains a deep convolutional neural network to predict if a given concept (e.g. a patera, a shield, a cornucopia) is depicted on an unknown coin. To validate whether the network is successfully identifying the salient pixels, heatmaps are generated using an occlusion technique to visualise which areas of the image are most important for the prediction made.