Ceramic Fabric Classification of Petrographic Thin Sections with Deep Learning
Classification of ceramic fabrics has long held a major role in archaeological pursuits. It helps answer research questions related to ceramic technology, provenance, and exchange and provides an overall deeper understanding of the ceramic material at hand. One of the most effective means of classif...
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| Format: | Article |
| Language: | English |
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Ubiquity Press
2021-09-01
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| Series: | Journal of Computer Applications in Archaeology |
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| Online Access: | https://journal.caa-international.org/articles/75 |
| _version_ | 1818743901350002688 |
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| author | Mike Lyons |
| author_facet | Mike Lyons |
| author_sort | Mike Lyons |
| collection | DOAJ |
| description | Classification of ceramic fabrics has long held a major role in archaeological pursuits. It helps answer research questions related to ceramic technology, provenance, and exchange and provides an overall deeper understanding of the ceramic material at hand. One of the most effective means of classification is through petrographic thin section analysis. However, ceramic petrography is a difficult and often tedious task that requires direct observation and sorting by domain experts. In this paper, a deep learning model is built to automatically recognize and classify ceramic fabrics, which expedites the process of classification and lessens the requirements on experts. The samples consist of images of petrographic thin sections under cross-polarized light originating from the Cocal-period (AD 1000–1525) archaeological site of Guadalupe on the northeast coast of Honduras. Two convolutional neural networks (CNNs), VGG19 and ResNet50, are compared against each other using two approaches to partitioning training, validation, and testing data. The technique employs a standard transfer learning process whereby the bottom layers of the CNNs are pre-trained on the ImageNet dataset and frozen, while a single pooling layer and three dense layers are added to ‘tune’ the model to the thin section dataset. After selecting fabric groups with at least three example sherds each, the technique can classify thin section images into one of five fabric groups with over 93% accuracy in each of four tests. The current results indicate that deep learning with CNNs is a highly accessible and effective method for classifying ceramic fabrics based on images of petrographic thin sections and that it can likely be applied on a larger scale. |
| first_indexed | 2024-12-18T02:35:46Z |
| format | Article |
| id | doaj.art-8ecb40921af94ed2bc194c22110f5e49 |
| institution | Directory Open Access Journal |
| issn | 2514-8362 |
| language | English |
| last_indexed | 2024-12-18T02:35:46Z |
| publishDate | 2021-09-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | Journal of Computer Applications in Archaeology |
| spelling | doaj.art-8ecb40921af94ed2bc194c22110f5e492022-12-21T21:23:48ZengUbiquity PressJournal of Computer Applications in Archaeology2514-83622021-09-014110.5334/jcaa.7556Ceramic Fabric Classification of Petrographic Thin Sections with Deep LearningMike Lyons0German Archaeological Institute, University of BonnClassification of ceramic fabrics has long held a major role in archaeological pursuits. It helps answer research questions related to ceramic technology, provenance, and exchange and provides an overall deeper understanding of the ceramic material at hand. One of the most effective means of classification is through petrographic thin section analysis. However, ceramic petrography is a difficult and often tedious task that requires direct observation and sorting by domain experts. In this paper, a deep learning model is built to automatically recognize and classify ceramic fabrics, which expedites the process of classification and lessens the requirements on experts. The samples consist of images of petrographic thin sections under cross-polarized light originating from the Cocal-period (AD 1000–1525) archaeological site of Guadalupe on the northeast coast of Honduras. Two convolutional neural networks (CNNs), VGG19 and ResNet50, are compared against each other using two approaches to partitioning training, validation, and testing data. The technique employs a standard transfer learning process whereby the bottom layers of the CNNs are pre-trained on the ImageNet dataset and frozen, while a single pooling layer and three dense layers are added to ‘tune’ the model to the thin section dataset. After selecting fabric groups with at least three example sherds each, the technique can classify thin section images into one of five fabric groups with over 93% accuracy in each of four tests. The current results indicate that deep learning with CNNs is a highly accessible and effective method for classifying ceramic fabrics based on images of petrographic thin sections and that it can likely be applied on a larger scale.https://journal.caa-international.org/articles/75aiceramic fabricsconvolutional neural networkdeep learningpetrographypottery |
| spellingShingle | Mike Lyons Ceramic Fabric Classification of Petrographic Thin Sections with Deep Learning Journal of Computer Applications in Archaeology ai ceramic fabrics convolutional neural network deep learning petrography pottery |
| title | Ceramic Fabric Classification of Petrographic Thin Sections with Deep Learning |
| title_full | Ceramic Fabric Classification of Petrographic Thin Sections with Deep Learning |
| title_fullStr | Ceramic Fabric Classification of Petrographic Thin Sections with Deep Learning |
| title_full_unstemmed | Ceramic Fabric Classification of Petrographic Thin Sections with Deep Learning |
| title_short | Ceramic Fabric Classification of Petrographic Thin Sections with Deep Learning |
| title_sort | ceramic fabric classification of petrographic thin sections with deep learning |
| topic | ai ceramic fabrics convolutional neural network deep learning petrography pottery |
| url | https://journal.caa-international.org/articles/75 |
| work_keys_str_mv | AT mikelyons ceramicfabricclassificationofpetrographicthinsectionswithdeeplearning |