research-article

Automatic classification of archaeological pottery sherds

Authors:

Michael Makridis,

Petros DarasAuthors Info & Claims

Journal on Computing and Cultural Heritage (JOCCH), Volume 5, Issue 4

Article No.: 15, Pages 1 - 21

https://doi.org/10.1145/2399180.2399183

Published: 09 January 2013 Publication History

Abstract

This article presents a novel technique for automatic archaeological sherd classification. Sherds that are found in the field usually have little to no visible textual information such as symbols, graphs, or marks on them. This makes manual classification an extremely difficult and time-consuming task for conservators and archaeologists. For a bunch of sherds found in the field, an expert identifies different classes and indicates at least one representative sherd for each class (training sample). The proposed technique uses the representative sherds in order to correctly classify the remaining sherds. For each sherd, local features based on color and texture information are extracted and are then transformed into a global vector that describes the whole sherd image, using a new bag of words technique. Finally, a feature selection algorithm is applied that locates features with high discriminative power. Extensive experiments were performed in order to verify the effectiveness of the proposed technique and show very promising results.

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Cited By

Ling ZDelnevo GSalomoni PMirri S(2024)Findings on Machine Learning for Identification of Archaeological Ceramics: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2024.342962312(100167-100185)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3429623
Gigilashvili DLukesova HGulbrandsen CHarijan AHardeberg J(2023)Computational techniques for virtual reconstruction of fragmented archaeological textilesHeritage Science10.1186/s40494-023-01102-311:1Online publication date: 13-Dec-2023
https://doi.org/10.1186/s40494-023-01102-3
Brahim KTreuillet SExbrayat MJesset S(2023)Facsimiles-based deep learning for matching relief-printed decorations on medieval ceramic sherds2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)10.1109/ICCVW60793.2023.00176(1605-1614)Online publication date: 2-Oct-2023
https://doi.org/10.1109/ICCVW60793.2023.00176
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Index Terms

Automatic classification of archaeological pottery sherds
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation

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Published In

cover image Journal on Computing and Cultural Heritage

Journal on Computing and Cultural Heritage Volume 5, Issue 4

December 2012

87 pages

ISSN:1556-4673

EISSN:1556-4711

DOI:10.1145/2399180

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Copyright © 2013 ACM.

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Publication History

Published: 09 January 2013

Accepted: 01 July 2012

Revised: 01 April 2012

Received: 01 December 2011

Published in JOCCH Volume 5, Issue 4

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Cited By

Ling ZDelnevo GSalomoni PMirri S(2024)Findings on Machine Learning for Identification of Archaeological Ceramics: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2024.342962312(100167-100185)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3429623
Gigilashvili DLukesova HGulbrandsen CHarijan AHardeberg J(2023)Computational techniques for virtual reconstruction of fragmented archaeological textilesHeritage Science10.1186/s40494-023-01102-311:1Online publication date: 13-Dec-2023
https://doi.org/10.1186/s40494-023-01102-3
Brahim KTreuillet SExbrayat MJesset S(2023)Facsimiles-based deep learning for matching relief-printed decorations on medieval ceramic sherds2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)10.1109/ICCVW60793.2023.00176(1605-1614)Online publication date: 2-Oct-2023
https://doi.org/10.1109/ICCVW60793.2023.00176
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https://doi.org/10.1007/s00500-023-08528-8
Wei C(2022)Mask R-CNN-Oriented Pottery Display and Identification SystemComputational Intelligence and Neuroscience10.1155/2022/62882012022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6288201
Lu YZhou JMcDorman SZhang CScott DBukuts JWilder CSmith KWang S(2022)Snowvision: Segmenting, Identifying, and Discovering Stamped Curve Patterns from Fragments of PotteryInternational Journal of Computer Vision10.1007/s11263-022-01669-7130:11(2707-2732)Online publication date: 1-Nov-2022
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Cacciari IPocobelli G(2022)Machine Learning: A Novel Tool for ArchaeologyHandbook of Cultural Heritage Analysis10.1007/978-3-030-60016-7_33(961-1002)Online publication date: 1-Jan-2022
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Sakpere WRoccetti MGallerani VMarchetti N(2021)An Intelligent Machine-Driven Perspective to Archaeological Pottery ReassemblyEncyclopedia of Information Science and Technology, Fifth Edition10.4018/978-1-7998-3479-3.ch010(127-137)Online publication date: 2021
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