EigenCoin: sassanid coins classification based on Bhattacharyya distance
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917404324397056 |
|---|---|
| author | Allahverdi, Rahele Dehshibi, Mohammad Mahdi Bastanfard, Azam Akbarzadeh, Daryoosh |
| author_facet | Allahverdi, Rahele Dehshibi, Mohammad Mahdi Bastanfard, Azam Akbarzadeh, Daryoosh |
| contents | Solving pattern recognition problems using imbalanced databases is a hot topic, which entices researchers to bring it into focus. Therefore, we consider this problem in the application of Sassanid coins classification. Our focus is not only on proposing EigenCoin manifold with Bhattacharyya distance for the classification task, but also on testing the influence of the holistic and feature-based approaches. EigenCoin consists of three main steps namely manifold construction, mapping test data, and classification. Conducted experiments show EigenCoin outperformed other observed algorithms and achieved the accuracy from 9.45% up to 21.75%, while it has the capability of handling the over-fitting problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11932 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EigenCoin: sassanid coins classification based on Bhattacharyya distance Allahverdi, Rahele Dehshibi, Mohammad Mahdi Bastanfard, Azam Akbarzadeh, Daryoosh Computer Vision and Pattern Recognition Solving pattern recognition problems using imbalanced databases is a hot topic, which entices researchers to bring it into focus. Therefore, we consider this problem in the application of Sassanid coins classification. Our focus is not only on proposing EigenCoin manifold with Bhattacharyya distance for the classification task, but also on testing the influence of the holistic and feature-based approaches. EigenCoin consists of three main steps namely manifold construction, mapping test data, and classification. Conducted experiments show EigenCoin outperformed other observed algorithms and achieved the accuracy from 9.45% up to 21.75%, while it has the capability of handling the over-fitting problem. |
| title | EigenCoin: sassanid coins classification based on Bhattacharyya distance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.11932 |