In this work we tackle the problem of automatic recognition of ancient coin types using a semisupervised learning method, namely Graph Transduction Games. Such problem is complex, mainly due to the low inter-class and large intra-class variations and the task becomes even more complex due to lack of labeled large datasets from certain ancient ages. In this paper we propose a new dataset which is chiefly the extension of a previous one both in terms of quantity and diversity. Moreover, we propose a game-theoretic model that exploits both sides of a coin to achieve higher classification accuracy. We experimentally demonstrate that proposed approach brings performance improvement in this complex task even when few number of labelled images are available.

Two sides of the same coin: Improved ancient coin classification using Graph Transduction Games

Aslan S.
;
Vascon S.;Pelillo M.
2020

Abstract

In this work we tackle the problem of automatic recognition of ancient coin types using a semisupervised learning method, namely Graph Transduction Games. Such problem is complex, mainly due to the low inter-class and large intra-class variations and the task becomes even more complex due to lack of labeled large datasets from certain ancient ages. In this paper we propose a new dataset which is chiefly the extension of a previous one both in terms of quantity and diversity. Moreover, we propose a game-theoretic model that exploits both sides of a coin to achieve higher classification accuracy. We experimentally demonstrate that proposed approach brings performance improvement in this complex task even when few number of labelled images are available.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3724442
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