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Coarse-grained ancient coin classification using image-based reverse side motif recognition

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Abstract

In this paper, we propose a novel approach for the image-based classification of ancient coins by recognizing motifs minted on their reverse sides. Reverse motif recognition is achieved with dense sampling-based bag-of-visual-words model. As the bag-of-visual-words model lacks spatial information of visual words, we evaluate three types of tiling schemes to incorporate such information. Due to the specific geometric structures of the objects in the reverse motifs, spatial information is also important for a better motif recognition rate. Furthermore, a coin can be imaged under various rotations. We also investigate the type of spatial scheme more robust to such rotations. Other parameters such as the size of visual vocabulary, resolution of the dense sampling grid, number of features collected per image to construct the visual vocabulary and the number of tilings in each tiling scheme are also investigated.

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Notes

  1. http://www.khm.at (last accessed Dec 20, 2013).

    Fig. 1
    figure 1

    Reverse motifs used for coarse-grained ancient coin classification

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    Fig. 2
    figure 2

    Dissimilarities among the motifs of the same type

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  2. http://www.britishmuseum.org (last accessed Dec 20, 2013).

  3. http://www.acsearch.info (last accessed Dec 20, 2013).

References

  1. Anwar, H., Zambanini, S., Kampel, M.: Supporting ancient coin classification by image-based reverse side symbol recognition. CAIP 2, 17–25 (2013)

    MathSciNet  Google Scholar 

  2. Arandjelović, O.: Automatic attribution of ancient Roman imperial coins. In: CVPR, pp. 1728–1734 (2010)

  3. Arandjelović, O.: Reading ancient coins: automatically identifying denarii using obverse legend seeded retrieval. In: ECCV, pp. 317–330 (2012)

  4. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)

  5. Crawford, M.H.: Roman Republican Coinage, 2 vols. Cambridge University Press, Cambridge (1974)

  6. Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV, pp. 1–22 (2004)

  7. Fan, B., Wu, F., Hu, Z.: Rotationally invariant descriptors using intensity order pooling. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 2031–2045 (2012)

    Article  Google Scholar 

  8. Huber, R., Ramoser, H., Mayer, K., Penz, H., Rubik, M.: Classification of coins using an eigenspace approach. Pattern Recogn. Lett. 26(1), 61–75 (2005)

    Article  Google Scholar 

  9. Kampel, M., Zaharieva, M.: Recognizing ancient coins based on local features. In: ISVC, LNCS, vol. I, pp. 11–22. Springer, Berlin (2008)

  10. Kavelar, A., Zambanini, S., Kampel, M.: Word detection applied to images of ancient roman coins. In: VSMM, pp. 577–580 (2012)

  11. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006)

  12. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)

    Article  Google Scholar 

  13. van der Maaten, L.J.P., Boon, P.J.: COIN-O-MATIC: a fast system for reliable coin classification. In: MUSCLE CIS Coin Competition Workshop, pp. 7–17 (2006)

  14. Nölle, M., Penz, H., Rubik, M., Mayer, K., Holländer, I., Granec, R.: Dagobert—a new coin recognition and sorting system. In: DICTA, pp. 329–338 (2003)

  15. Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: ECCV, pp. 490–503 (2006)

  16. Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. ICCV 2, 1470–1477 (2003)

    Google Scholar 

  17. Vedaldi, A., Zisserman, A.: Sparse kernel approximations for efficient classification and detection. In: CVPR (2012)

  18. Wang, Z., Fan, B., Wu, F.: Local intensity order pattern for feature description. In: ICCV, pp. 603–610 (2011)

  19. Zaharieva, M., Kampel, M., Zambanini, S.: Image based recognition of ancient coins. In: CAIP, pp. 547–554 (2007)

  20. Zambanini, S., Kampel, M.: Coarse-to-fine correspondence search for classifying ancient coins. ACCV Workshops 2, 25–36 (2012)

    Google Scholar 

  21. Zhang, E., Mayo, M.: Enhanced spatial pyramid matching using log-polar-based image subdivision and representation. In: DICTA, pp. 208–213 (2010)

Download references

Acknowledgments

This research was supported by the Austrian Science Fund (FWF) under the grant TRP140-N23-2010 (ILAC) and the Vienna PhD School of Informatics (http://www.informatik.tuwien.ac.at/teaching/phdschool).

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Authors and Affiliations

  1. Computer Vision Lab, Institute of Computer Aided Automation, Vienna University of Technology, 1040, Vienna, Austria

    Hafeez Anwar, Sebastian Zambanini & Martin Kampel

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  1. Hafeez Anwar
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  2. Sebastian Zambanini
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  3. Martin Kampel
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Correspondence to Hafeez Anwar.

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Anwar, H., Zambanini, S. & Kampel, M. Coarse-grained ancient coin classification using image-based reverse side motif recognition. Machine Vision and Applications 26, 295–304 (2015). https://doi.org/10.1007/s00138-015-0665-2

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  • DOI: https://doi.org/10.1007/s00138-015-0665-2

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