Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Classification of coins using an eigenspace approach
Pattern Recognition Letters
Recognizing Ancient Coins Based on Local Features
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Image based recognition of ancient coins
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic coin classification by image matching
VAST'11 Proceedings of the 12th International conference on Virtual Reality, Archaeology and Cultural Heritage
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In this paper, we build upon the idea of using robust dense correspondence estimation for exemplar-based image classification and adapt it to the problem of ancient coin classification. We thus account for the lack of available training data and demonstrate that the matching costs are a powerful dissimilarity metric to establish coin classification for training set sizes of one or two images per class. This is accomplished by using a flexible dense correspondence search which is highly insensitive to local spatial differences between coins of the same class and different coin rotations between images. Additionally, we introduce a coarse-to-fine classification scheme to decrease runtime which would be otherwise linear to the number of classes in the training set. For evaluation, a new dataset representing 60 coin classes of the Roman Republican period is used. The proposed system achieves a classification rate of 83.3 % and a runtime improvement of 93 % through the coarse-to-fine classification.