Last modified: 2011-12-17
Abstract
Determining the type of an ancient coin is in general a time consuming task and needs a lot of numismatic experience. Therefore, an automatic method for this task would be of high usage for the numismatic community. 2D images can serve as input for such a system as they are easy and cheap to produce and are widely available in museum databases and digital online archives. Potentially, such a methodology is able to act as a supporting tool for numismatists and can thus enable a much faster processing of coins. In the long run, an automatic image based coin classification system could be of use for a broad range of the numismatic community, e.g. by means of a freely accessible online coin classification tool.
This paper presents a method which uses image analysis in order to determine the classes of the coins. We first discuss the specific challenges that ancient coins pose to an image-based classification method due to their high level of degradation and variability. Accordingly, we argue to use image matching for this task, as it is able to measure the similarity of coins between images and can consequently be used for classification: the method measures the similarity between a coin image and all coin images in the training set (the database) and finally chooses the class with the highest image similarity. We use a flexible image matching method which is able to deal with the local spatial variations of features within a class. The method is based on local SIFT features which are extracted on a dense field in the image and matched by means of optimization of a cost function.
The proposed method is evaluated on a database of early Roman Republican coinage and the strengths and the weaknesses of the method are discussed based on the performance achieved. Finally, we give an outlook on future work that is assumed to lead to a comprehensive systematical approach for automatic coin classification.