1. Towards automatic coin classification
L.J.P. van der Maaten
E.O. Postma

2. Introduction RICH project (Reading Images for the Cultural Heritage)
Initiated by NWO-CATCH (grant )
Institutions involved:
MICC-IKAT (Maastricht University)
ROB (Dutch State Service for Archaeology)
People involved:
E.O. Postma, A.G. Lange, H.H. Paijmans, L.J.P. van der Maaten, P.J. Boon

3. Introduction Automatic coin classification based on visual features
May allow sorting heterogeneous coin collections, both modern and historical
For modern coins, applications for charity organizations, financial institutions, and change offices (MUSCLE CIS benchmark)
For historical coins, applications in the cultural heritage domain

4. Introduction
Currently, some coin historical collections are being disclosed on the Internet, e.g., in NUMIS1
NUMIS website shows information (and sometimes photographs) on collected coins
However, the use of such websites for non-experts is limited
1 A project of the Dutch Money Museum

5. Introduction
Non-experts who find a coin would like to know what sort of coin it is
i.e. coin classification based on visual features
Non-experts would benefit from a system for automatic coin classification
Also beneficial to experts to speed up and objectify the classification process

6. Introduction
A modern and a historical coin photograph

7. Introduction This presentation
Presents a number of features that can be used for the classification of modern coins
Shows promising results for these features
Investigates the performance of the same features on a medieval coin dataset
Tries to provide some insight in why the features fail on the medieval coin data

8. Features Contour features Texture features Edge distance distributions
Edge angle distributions
Edge angle-distance distributions
Texture features
Gabor histograms
Daubechies D4 wavelet features

9. Contour features Measure statistical distributions of edge pixels
Edge pixels computed using Sobel filter convolution (with non-maxima suppression and dynamic thresholding)
Coin borders are removed

10. Edge distance distributions
Estimate the distribution of the distances of edge pixels to the center of the coin
Rotation invariant feature
Can be measured on coarse-to-fine-scales

11. Edge angle distributions
Measure distribution of angles of edge pixels w.r.t. the baseline
Not rotation invariant by definition (however, the magnitude of the Fourier transform is)
Can be measured on number of fine scales

12. Edge angle-distance distr.
Incorporate both angular and distance information in the coin stamp
We measure EADD using 2, 4, 8, and 16 distance bins and 180 angular bins

13. Gabor histograms
Convolution of coin image with Gabor filters of various scales and rotations
Compute image histograms of the resulting convolution images
Apply PCA for dimensionality reduction (200 dimensional)

14. Daubechies D4 wavelet
Perform wavelet decomposition using Daubechies D4 wavelet
Computed wavelet coefficients are used as features (2-, 3-, and 4-level; ahvd)
Do this for 16 rotated versions of the coins in the training set (for rotation invariance)
Apply PCA for dimensionality reduction (results in 200-dimensional feature vector)

15. Experiments Performed on the MUSCLE CIS benchmark coin dataset1
The dataset contains 692 coin classes with 2,270 coin faces
Training set of 20,000 coins
Test set of 5,000 coins
Incorporate area measurements
1 Newer experiments than the ones described in the paper

16. Experiments

17. Experiments Classification performances (5-NN classifier)
Edge distance distributions
68%
Edge angle distributions
62%
Edge angle-distance distributions
78%
Gabor histograms
55%
Daubechies D4 wavelet features
46%

18. Experiments
Subsequently, we performed experiments on the Merovingen dataset1
Contains 4,569 early-medieval coins
Class distribution skewed
Experiments using 10-fold cross validation
1 Dataset property of Dutch Money Museum

19. Experiments Skewed class distributions Class type No. of classes
Mean class size
St. dev. of class size
City 18 53
125
Mint master 19 69
121
Currency 4
859
1,469
Nation 12
199
438

20. Experiments Classification performances (naïve Bayes classifier)
Feature
City
Mint master
Currency
Nation
Area
16%
10%
61%
17%
Edge dist. distr.
12% 8%
50%
20%
Edge angle distr. 6%
34%
14%
Gabor histogram 5%
25%
D4 wavelet feat.

21. Discussion Although results on modern coin data are promising
Results on Merovingen coin dataset disappointing

22. Discussion Reasons for results on medieval coins:
Contour features highly rely on the correct estimation of the center of the stamp
Texture features more suitable for coins with detailed artwork in stamps
Errors and inconsistencies in these kind of datasets

23. Discussion
Coin classified as Frankish
Coin classified as Frisian

24. Discussion Reasons for results on medieval coins :
Medieval coins have larger within-class variances due to quick deterioration of medieval coin stamps
Medieval coins have smaller between-class variances (stamps often contain similar pictures, such as a cross or the head of an authority)

25. Discussion Reasons for results on medieval coins :
Experts indicate that classifications are based on small details
I.e. expert classifications are based on a large number of small (undocumented) rules
Experts (consciously or not) take extrinsic information into account (such as finding location)

26. Discussion
How should a system for automatic classification of medieval coins work?
Text is highly discriminating, however, cannot be read by state-of-the-art in character recognition
We foresee the development of a semi-automatic adaptive system in which the expert indicates distinguishing features of the coin
Over time, the system should be able to learn the undocumented rules

27. Conclusions
Contour and texture features perform well in the classification of modern coins
The results of these features on early-medieval coins are disappointing
There are various reasons why the features fail in the classification of medieval coins
Future work: semi-automatic approach

28. Questions ?