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Coarse-grained correspondence-based ancient Sasanian coin classification by fusion of local features and sparse representation-based classifier

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Abstract

Numismatics sorts out historical aspects of money. Identification and classification of coins, as a part of their duties, need years of experience. This research aims at using the knowledge of numismatics for developing an image-based classification of ancient Sassanian dynasty coins. A straightforward method is to take coins observe and reverse-side motifs into account, just like numismatics does. To this aim, three feature descriptors, Cosine transform, Wavelet transform and Bi-Directional Principal Component Analysis, are separately applied to the extracted motifs’ areas to form the feature space. To cope with the ‘curse of dimensionality’ and increase the ‘discrimination power’, feature space is enriched with spatial information achieved by applying a feature selection method. Indeed, the best feature subset, which maximizes the mutual information between the joint distribution of the selected features and the classification variable, is selected using the minimum Redundancy Maximum Relevance (mRMR) method to a trade-off between thousands of features and a few hundreds of samples. One fold of our contribution dedicates to decrease the over-fitting probability of the learning model by making the Sparse Representation-based Classifier kernelized. We evaluate our method on a dataset of 573 coin images. The experimental results show that our proposed image representation is more discriminative than the competitive ones in which the system achieves a mean classification rate of 96.51 %.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Seyyedeh-Sahar Parsa & Mohammad Mahdi Dehshibi

  2. Young Researchers and Elite Club, Zahedan Branch, Islamic Azad University, Zahedan, Iran

    Mohamad Sourizaei

  3. Department of Mechatronics, Ahar Branch, Islamic Azad University, Ahar, Iran

    Reza Esmaeilzadeh Shateri

  4. Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran

    Mohammad Reza Parsaei

Authors
  1. Seyyedeh-Sahar Parsa
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  2. Mohamad Sourizaei
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  3. Mohammad Mahdi Dehshibi
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  4. Reza Esmaeilzadeh Shateri
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  5. Mohammad Reza Parsaei
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Correspondence to Seyyedeh-Sahar Parsa or Mohamad Sourizaei.

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Parsa, SS., Sourizaei, M., Dehshibi, M. et al. Coarse-grained correspondence-based ancient Sasanian coin classification by fusion of local features and sparse representation-based classifier. Multimed Tools Appl 76, 15535–15560 (2017). https://doi.org/10.1007/s11042-016-3856-6

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  • DOI: https://doi.org/10.1007/s11042-016-3856-6

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