Recognition of Ancient Roman Coins using Spatial Information

Recognition of Ancient Roman Coins

1. Problem Formulation

For a given Roman coin image, the goal is to recognize who is on the coin

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There are thousand of different ways to define the Roman coins. For example, we can classify the coins by attributes such as symbols, sizes, materials and legend. Please note that those attributes are correlated together. One attribute may help reveal the other attributes. In this project, we focus on a face recognition problem where for a given Roman coin image, the goal is to recognize who is on the coin. So, for above images, we want to know that the Roman emperor, Caligular, is engraved on the coin. This is for Maximus second, the famous emperors Nero and Tiberius.

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2. Motivation

Understanding the ancient Roman coins could serve as references to understand the Roman empire

  • The Roman coins are always connected to Roman historical events and Roman imperial propaganda
  • The Roman empire knew how to effectively use the coin as their political propaganda
  • The Roman coins were widely used to convey the achievements of Roman emperors to public
  • The Roman coins were served to spread messages of changing policies or merits through the empire
  • The Roman emperors also could show themselves to the entire empire by engraving portraits on the coins
  • The Roman coins were the newspaper of the Roman empire

3. Practical Application

A reliable and automatic method to recognize the coins is necessary

  • The coin market is very active as many people are collecting coins as hobby. Also the coins were massively produced and new Roman coins are daily excavated, making themselves affordable to collect.
  • Ancient coins are becoming subject to a very large illicit trade. Recognition of the ancient Roman coins is not easy for novices but requires knowledge.
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  • A traditional way is to periodically and manually search catalogue, dealers or the Internet by authority forces.
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4. Challenges

  • Inter-class similarity due to engraver’s lack of knowledge for the emperor’s portrait and abstraction
  • Intra-class dissimilarity. The coins were made manually from different factories
  • The recognition of the face on the coin is different from that of the real face

5. Coin Data Collection

  • Coin images are collected from a numismatic website [1, 2]
  • 2815 coin images with 15 Roman emperors
  • – Small part of the much larger dataset
    – Annotated for visual analysis (the original dataset only has numismatic annotation)
    – Each emperor has at least 10 coin images

  • High resolution images : 350-by-350 pixels

6. Coin Recognition Methods using Spatial Information

  1. Deformable Part Model (DPM) based method
  2. – Precise encoding of spatial information more specifically than spatial pyramid by alignment
    – DPM is used to align the coin image by locating the face of the emperor
    – Training and test of DPM

  3. Fisher Vector based method
  4. – Each point is presented as a combination of visual features and location, (x, l)
    – Gaussian mixture model to describe probability of (x, l)
    \[ \begin{eqnarray} p(\mathbf{x}, \mathbf{l}) & = & \sum_k \pi_k \cdot p(\mathbf{x}, \mathbf{l}; {\Sigma}_k^V, {\Sigma}_k^L, {\mu}_k^V, {\mu}_k^L) \nonumber \\ & = & \sum_k \pi_k \cdot p(\mathbf{x}; {\Sigma}_k^V, {\mu}_k^V) \cdot p(\mathbf{l}; {\Sigma}_k^L, {\mu}_k^L), \end{eqnarray} \] where \(\pi_k\) is a prior probability for the \(k\)th component, \({\Sigma}_k^V, {\mu}_k^V\) are means and covariances for the visual descriptors, \({\Sigma}_k^L, {\mu}_k^L\) mean and covariance for the location, and \[ \begin{eqnarray} p(\mathbf{x}; {\Sigma}_k^V, {\mu}_k^V) & \quad \sim \quad & \mathcal{N} (\mathbf{x}; {\Sigma}_k^V, {\mu}_k^V)\\ p(\mathbf{l}; {\Sigma}_k^L, {\mu}_k^L) & \quad \sim \quad & \mathcal{N} (\mathbf{l}; {\Sigma}_k^L, {\mu}_k^L). \end{eqnarray} \] The gradient with respect to the mu and sigma defines the Fisher vector.

7. Experimental Results

  • Experimental settings
  • – 2815 coin images with 15 emperors
    – For evaluation, divide the coin dataset into 5 fold splits, training on 4 splits and testing on 1 split
    – SIFT as visual feature
    – Multi-class SVM for training and prediction

  • Recognition accuracies for various methods
  • Confusion matrices
  • Discriminative regions
  • Outlier detection

8. Conclusion

We proposed two automatic methods to recognize the ancient Roman coins. The first method employs the deformable part model to align the coin images to improve the recognition accuracy. The second method facilitates the spatial information of the coin by directly encoding the location information. As the first method takes the information of the face location into account, it performs slightly better than the second method. The experiments show that both methods outperform the other methods such as the standard spatial pyramid model and human face recognition method.

In this project, we collect a new ancient Roman coin dataset and investigate an automatic framework to recognize the coins where we employ the state-of-the-art face recognition system and facilitate the spatial information of the coin to improve the recognition accuracy. The coin images are high-resolution (350-by-350 pixels) and the face locations are annotated. While the proposed coin recognition framework is based on the standard methods such as bag-of-words with spatial pyramids, Fisher vectors and DPM, we believe that their use in the context of the ancient coin recognition represents an interesting contribution.

References

  • [1] J. Kim and V. Pavlovic. “Ancient Coin Recognition Based on Spatial Coding”. Proc. International Conference on Pattern Recognition (ICPR). 2014.
  • [2] J. Kim and V. Pavlovic. “Improving Ancient Roman Coin Recognition with Alignment and Spatial Encoding”. ECCV Workshop VISART. 2014.

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