Abstract
In Greek art, the phase from 900 to 700 BCE is referred to as the Geometric period due to the characteristically simple geometry-like ornamentations appearing on painted pottery surfaces during this era. Distinctive geometric patterns are typical for specific periods, regions, workshops as well as painters and are an important cue for archaeological tasks, such as dating and attribution. To date, these analyses are mostly conducted with the support of information technology. The primitives of an artefact’s ornamentation can be generally classified into a set of distinguishable pattern classes, which also appear in a similar fashion on other objects. Although a taxonomy of known pattern classes is given in subject-specific publications, the automatic detection and classification of surface patterns from object depictions poses a non-trivial challenge. Our long-term goal is to provide this classification functionality using a specifically designed and trained neural network. This, however, requires a large amount of labelled training data, which at this point does not exist for this domain context. In this work, we propose an effective annotation system, which allows a domain expert to interactively segment and label parts of digitized vessel surfaces. These user inputs are constantly fed back to a Convolutional Neural Network (CNN), enabling the prediction of pattern classes for a given surface area with ever increasing precision. Our work paves the way for a fully automatic classification and analysis of large surface pattern collections, which, with the help of suitable visual analysis techniques, can answer research questions like pattern variability or change over time. While the capability of our proposed annotation pipeline is demonstrated at the example of two characteristic Greek pottery artefacts from the Geometric period, the proposed methods can be readily adopted for the patternation in any other chronological periods as well as for stamped motifs.
Funding source: Austrian Science Fund
Funding source: Agencia Nacional de Investigación y Desarrollo
Award Identifier / Grant number: ICN17_002
Funding statement: This article was produced within the interdisciplinary project “Crossmodal Search and Visual Exploration of 3D Cultural Heritage Objects” (P31317-NBL) co-funded by the Austrian Science Fund FWF and the State of Styria, Austria. Benjamin Bustos was funded by ANID – Millennium Science Initiative Program – Code ICN17_002.
About the authors
Dipl.-Ing. Stefan Lengauer is doing his PhD in the field of crossmodal search and visual exploration of cultural heritage objects. Since 2018 he is a member at Institute of Computer Graphics and Knowledge Visualization and has authored several publications on the topic of shape-based and motif-based retrieval, as well as interactive visualization and reconstruction of ancient pottery.
Dipl.-Ing. Peter Houska is a PhD student at the Institute of Computer Graphics and Knowledge Visualization at Graz University of Technology. His research interests include interactive visualization, geometry processing, and digital archaeology.
Dr. techn. Reinhold Preiner is a senior researcher in computer science, specialized in 3D computer graphics and visualization. He received his PhD from TU Wien in 2017 and is now with the Institute of Computer Graphics and Knowledge Visualization at Graz University of Technology. His main fields of research include applied geometry processing, rendering, and interactive visualization, involving beside others applications in digital archaeology.
Dr. phil. Elisabeth Trinkl is a staff member of the Institute of Classics at Graz University. She received her PhD in Classical Archaeology at Vienna University in 1998. Her research interests focus on Greek archaeology in general, the understanding of ancient textiles and the use of computer-assisted methods in archaeological research. She is author of several articles on these topics, a. o. of a volume of the international publication series Corpus Vasorum Antiquorum (CVA), and editor of collections of essays.
Dr. phil. Stephan Karl is a scientific member of the Institute of Classics at Graz University. He received his PhD in Classical Archaeology in 2013. His research interests include the study of Early Greek pottery, Roman provincial stone monuments and Roman marble quarries, with a special focus on the application of 3D technologies for visualising and analysing purposes in archaeological research. He is a. o. author of two volumes of the international publication series Corpus Vasorum Antiquorum (CVA).
Prof. Dr. Ivan Sipiran is an Assistant Professor at the Department of Computer Science, University of Chile. He received the PhD in computer science from the University of Chile in 2014. He works in the areas of geometry processing, shape analysis and the application of computer graphics in cultural heritage.
Prof. Dr. Benjamin Bustos is a Full Professor at the Department of Computer Science, University of Chile, and Associate Researcher at the Millennium Institute Foundational Research on Data. He received the Doctoral degree in natural sciences from the University of Konstanz, Germany, in 2006. He leads research projects in the domain of content-based multimedia information retrieval. His research interests include similarity search, 3D object retrieval, multimedia mining, metric/nonmetric indexing, and pattern recognition.
Prof. Dr. Tobias Schreck is a Professor and head of the Institute of Computer Graphics and Knowledge Visualization at Graz University of Technology. He previously was Assistant Professor with University of Konstanz, Germany, and Postdoc Fellow with Technical University of Darmstadt. He obtained a PhD in Computer Science in 2006 from the University of Konstanz. Tobias Schreck works in the areas of Visual Analytics, Information Visualization, and Applied 3D Object Retrieval.
Acknowledgment
We are very grateful to the editors and the reviewers for their valuable comments and suggestions that helped to improve the manuscript.
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