Paper Info
Title
Ancient Coin Classification Using Reverse Motif Recognition: Image-based classification of Roman Republican coins
Abstract
We propose a holistic system to classify ancient Roman Republican coins based on their reverse-side motifs. The bag-of-visual-words (BoW) model is enriched with spatial information to increase the discriminative power of the coin image representation. This is achieved by combining a spatial pooling scheme with co-occurrence encoding of visual words. We specifically address the required geometric invariance properties of image-based ancient coin classification, as coins from different collections can be located at differing image locations, have various scales in the images and can undergo various in-plane rotations. We evaluate our method on a data set of 2,224 coin images from three different sources. The experimental results show that our proposed image representation is more discriminative than the traditional bag-of-visual-words model while still being invariant to the mentioned geometric transformations. For 29 motifs, the system achieves a classification rate of 82%. It is considered to act as a helpful tool for numismatists in the near future, which facilitates and supports the traditional coin classification process by a faster presorting of coins.
Year
DOI
Venue
2015
10.1109/MSP.2015.2409331
Signal Processing Magazine, IEEE
Keywords
Field
DocType
histograms,classification,spatial information,visualization,image classification,image segmentation,art,bag of visual words
Spatial analysis,Computer vision,Pattern recognition,Computer science,Transformation geometry,Pooling,Motif (music),Invariant (mathematics),Artificial intelligence,Discriminative model,Encoding (memory),Visual Word
Journal
Volume
Issue
ISSN
32
4
1053-5888
Citations 
PageRank 
References 
4
0.44
17
Authors
4
Name
Order
Citations
PageRank
Hafeez Anwar140.44
Sebastian Zambanini2111.98
Martin Kampel3121.85
Klaus Vondrovec440.44