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

Aslan, Sinem; Vascon, Sebastiano; Pelillo, Marcello

doi:10.1016/j.patrec.2019.12.007

Cited by 20 publications

(18 citation statements)

References 25 publications

(59 reference statements)

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“…In [17], the authors classify ancient Roman Republican coins, matching a dense set of SIFT features extracted from a picture against a database. Another work for Roman Republican coins using SIFT classification is found in [18,19], where the authors classify the coins in 60 categories. The SIFT method has been evaluated in our work, giving worse results than YOLOv3 for our problem.…”

Section: Overview Of Related Workmentioning

confidence: 99%

Egyptian Shabtis Identification by Means of Deep Neural Networks and Semantic Integration with Europeana

2020

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Ancient Egyptians had a complex religion, which was active for longer than the time that has passed since Cleopatra until our days. One amazing belief was to be buried with funerary statuettes to help the deceased carry out his/her tasks in the underworld. These funerary statuettes, mainly known as shabtis, were produced in different materials and were usually inscribed in hieroglyphs with formulas including the name of the deceased. Shabtis are important archaeological objects which can help to identify the owners, their jobs, ranks or their families. They are also used for tomb dating because, depending on different elements: color, formula, tools, wig, hand positions, etc., it is possible to associate them to a concrete type or period of time. Shabtis are spread all over the world, in excavations, museums or private collections, and many of them have not been studied and identified because this process requires a deep study and reading of the hieroglyphs. Our system is able to solve this problem using two different YOLO v3 networks for detecting the figure itself and the hieroglyphic names, which provide identification and cataloguing. Until now, there has been no other work on the detection and identification of shabtis. In addition, a semantic approach has been followed, creating an ontology to connect our system with the semantic metadata aggregator, Europeana, linking our results with known shabtis in different museums. A complete dataset has been created, a comparison with previous technologies for similar problems has been provided, such as SIFT in the ancient coin classification, and the results of identification and cataloguing are shown. These results are over similar problems and have led us to create a web application that shows our system and is available on line.

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Section: Overview Of Related Workmentioning

confidence: 99%

Egyptian Shabtis Identification by Means of Deep Neural Networks and Semantic Integration with Europeana

2020

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“…Considering the focus of the present work, that is, the examination of the relevant information content of the modality (namely colour) under the consideration and our different approaches to the representation thereof, as well as the lack of any prior investigation of the same in the existing literature, we decided to opt for a simple and readily interpretable classification approach in the form of random forests [16] rather than one of the recent deep-learning-based methods, despite their recent successes in this domain [17][18][19][20]. We used a forest consisting of 30 random trees.…”

Section: Experimental Methodology and Detailsmentioning

confidence: 99%

Classification of Ancient Roman Coins by Denomination Using Colour, a Forgotten Feature in Automatic Ancient Coin Analysis

Ma¹,

Arandjelović²

2020

Sci

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Ancient numismatics, that is, the study of ancient currencies (predominantly coins), is an interesting domain for the application of computer vision and machine learning, and has been receiving an increasing amount of attention in recent years. Notwithstanding the number of articles published on the topic, the variety of different methodological approaches described, and the mounting realisation that the relevant problems in the field are most challenging indeed, all research to date has entirely ignored one specific, readily accessible modality: colour. Invariably, colour is discarded and images of coins treated as being greyscale. The present article is the first one to question this decision (and indeed, it is a decision). We discuss the reasons behind the said choice, present a case why it ought to be reexamined, and in turn investigate the issue for the first time in the published literature. Specifically, we propose two new colour-based representations specifically designed with the aim of being applied to ancient coin analysis, and argue why it is sensible to employ them in the first stages of the classification process as a means of drastically reducing the initially enormous number of classes involved in type matching ancient coins (tens of thousands, just for Ancient Roman Imperial coins). Furthermore, we introduce a new data set collected with the specific aim of denomination-based categorisation of ancient coins, where we hypothesised colour could be of potential use, and evaluate the proposed representations. Lastly, we report surprisingly successful performances which goes further than confirming our hypothesis—rather, they convincingly demonstrate a much higher relevant information content carried by colour than even we expected. Thus we trust that our findings will be noted by others in the field and that more attention and further research will be devoted to the use of colour in automatic ancient coin analysis.

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“…Thus, Schlag and Arandjelović [17] proposed a VGG16 deep-neural-network-based algorithm for issuing authority recognition, and demonstrated outstanding performance on three large corpora of data. Aslan et al [16] used a pre-trained ImageNet, adapted to the domain using transfer learning, on a small data set of Roman republican coins with lesser success. The deep learning algorithms of Cooper and Arandjelović [18,20] and Anwar et al [35] both focus on the semantics of motifs depicted on coins, the former on Roman imperial and the latter on Roman republican coins-the problem which we already noted as being extremely promising in terms of practical significance, and most interesting from the technical viewpoint.…”

Section: Research Effort To Datementioning

confidence: 99%

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

Arandjelović¹,

Zachariou²

2020

Sci

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Automatic ancient Roman coin analysis only recently emerged as a topic of computer science research. Nevertheless, owing to its ever-increasing popularity, the field is already reaching a certain degree of maturity, as witnessed by a substantial publication output in the last decade. At the same time, it is becoming evident that research progress is being limited by a somewhat veering direction of effort and the lack of a coherent framework which facilitates the acquisition and dissemination of robust, repeatable, and rigorous evidence. Thus, in the present article, we seek to address several associated challenges. To start with, (i) we provide a first overview and discussion of different challenges in the field, some of which have been scarcely investigated to date, and others which have hitherto been unrecognized and unaddressed. Secondly, (ii) we introduce the first data set, carefully curated and collected for the purpose of facilitating methodological evaluation of algorithms and, specifically, the effects of coin preservation grades on the performance of automatic methods. Indeed, until now, only one published work at all recognized the need for this kind of analysis, which, to any numismatist, would be a trivially obvious fact. We also discuss a wide range of considerations which had to be taken into account in collecting this corpus, explain our decisions, and describe its content in detail. Briefly, the data set comprises 100 different coin types, all with multiple examples in Fine, Very Fine, and Extremely Fine conditions, giving a total of over 650 different specimens. These correspond to 44 issuing authorities and span the time period of approximately 300 years (from 27 BC until 244 AD). In summary, the present article should be an invaluable resource to researchers in the field, and we encourage the community to adopt the collected corpus, freely available for research purposes, as a standard evaluation benchmark.

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

Cited by 20 publications

References 25 publications

Egyptian Shabtis Identification by Means of Deep Neural Networks and Semantic Integration with Europeana

Egyptian Shabtis Identification by Means of Deep Neural Networks and Semantic Integration with Europeana

Classification of Ancient Roman Coins by Denomination Using Colour, a Forgotten Feature in Automatic Ancient Coin Analysis

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

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