We perform classification of ancient Roman Republican coins via recognizing their reverse motifs where various objects, faces, scenes, animals, and buildings are minted along with legends. Most of these coins are eroded due to their age and varying degrees of preservation, thereby affecting their informative attributes for visual recognition. Changes in the positions of principal symbols on the reverse motifs also cause huge variations among the coin types. Lastly, in-plane orientations, uneven illumination, and a moderate background clutter further make the task of classification non-trivial and challenging. To this end, we present a novel network model, CoinNet, that employs compact bilinear pooling, residual groups, and feature attention layers. Furthermore, we gathered the largest and most diverse image dataset of the Roman Republican coins that contains more than 18,000 images belonging to 228 different reverse motifs. On this dataset, our model achieves a classification accuracy of more than and outperforms the conventional bag-of-visual-words based approaches and more recent state-of-the-art deep learning methods. We also provide a detailed ablation study of our network and its generalization capability.
Unlike legends and portraits, the reverse motif is a discriminative visual cue that is less affected by wear and tear. Besides, a given reverse motif can be shared by coins of multiple classes. Therefore, the search space for the class of a given query coin image is aptly reduced by recognizing its reverse motif. This aspect makes the reverse motif-based coin classification coarse-grained that can further be refined by fine-grained classification methods @cite_17 .
@cite_17
1509901792
We propose a holistic system to classify ancient Roman Republican coins based on their reverse-side motifs. The bag-of-visual-words (BoW) model is enriched with spatial information to increase the discriminative power of the coin image representation. This is achieved by combining a spatial pooling scheme with co-occurrence encoding of visual words. We specifically address the required geometric invariance properties of image-based ancient coin classification, as coins from different collections can be located at differing image locations, have various scales in the images and can undergo various in-plane rotations. We evaluate our method on a data set of 2,224 coin images from three different sources. The experimental results show that our proposed image representation is more discriminative than the traditional bag-of-visual-words model while still being invariant to the mentioned geometric transformations. For 29 motifs, the system achieves a classification rate of 82 . It is considered to act as a helpful tool for numismatists in the near future, which facilitates and supports the traditional coin classification process by a faster presorting of coins.