Computational Formalism
How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another.Though formalism is an essential tool for art historians, much recent art history has focused on the social...
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| Materyal Türü: | Online |
| Dil: | İngilizce |
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The MIT Press
2023
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| Online Erişim: | ONIX_20230731_9780262374736_34 |
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| _version_ | 1869526544471293952 |
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| author | Wasielewski, Amanda |
| author_browse | Wasielewski, Amanda |
| author_facet | Wasielewski, Amanda |
| author_sort | Wasielewski, Amanda |
| collection | Directory of Open Access Books |
| description | How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another.Though formalism is an essential tool for art historians, much recent art history has focused on the social and political aspects of art. But now art historians are adopting machine learning methods to develop new ways to analyze the purely visual in datasets of art images. Amanda Wasielewski uses the term “computational formalism” todescribe this use of machine learning and computer vision technique in art historical research. At the same time that art historians are analyzing art images in new ways, computer scientists are using art images for experiments in machine learning and computer vision. Their research, says Wasielewski, would be greatly enriched by the inclusion of humanistic issues.The main purpose in applying computational techniques such as machine learning to art datasets is to automate the process of categorization using metrics such as style, a historically fraught concept in art history. After examining a fifteen-year trajectory in image categorization and art dataset creation in the fields of machine learning and computer vision, Wasielewski considers deep learning techniques that both create and detect forgeries and fakes in art. She investigates examples of art historical analysis in the fields of computer and information sciences, placing this research in the context of art historiography. She also raises questions as which artworks are chosen for digitization, and of those artworks that are born digital, which works gain acceptance into the canon of high art. |
| format | Online |
| id | doab-20.500.12854ir-111600 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | The MIT Press |
| publisherStr | The MIT Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1116002024-03-23T21:36:16Z Computational Formalism Wasielewski, Amanda Art history artificial intelligence machine learning formalism digital humanities connoisseurship image database authentication style thema EDItEUR::A The Arts::AG The Arts: treatments and subjects::AGA History of art thema EDItEUR::3 Time period qualifiers::3M c 1500 onwards to present day::3MN 19th century, c 1800 to c 1899 thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::A The Arts::AF The Arts: art forms::AFK Non-graphic and electronic art forms::AFKV Digital, video and new media arts How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another.Though formalism is an essential tool for art historians, much recent art history has focused on the social and political aspects of art. But now art historians are adopting machine learning methods to develop new ways to analyze the purely visual in datasets of art images. Amanda Wasielewski uses the term “computational formalism” todescribe this use of machine learning and computer vision technique in art historical research. At the same time that art historians are analyzing art images in new ways, computer scientists are using art images for experiments in machine learning and computer vision. Their research, says Wasielewski, would be greatly enriched by the inclusion of humanistic issues.The main purpose in applying computational techniques such as machine learning to art datasets is to automate the process of categorization using metrics such as style, a historically fraught concept in art history. After examining a fifteen-year trajectory in image categorization and art dataset creation in the fields of machine learning and computer vision, Wasielewski considers deep learning techniques that both create and detect forgeries and fakes in art. She investigates examples of art historical analysis in the fields of computer and information sciences, placing this research in the context of art historiography. She also raises questions as which artworks are chosen for digitization, and of those artworks that are born digital, which works gain acceptance into the canon of high art. 2023-07-31T10:55:03Z 2023-07-31T10:55:03Z 2023 book ONIX_20230731_9780262374736_34 9780262374736 9780262545648 https://directory.doabooks.org/handle/20.500.12854/111600 eng Leonardo image/jpeg n/a https://doi.org/10.7551/mitpress/14268.001.0001 The MIT Press The MIT Press 10.7551/mitpress/14268.001.0001 10.7551/mitpress/14268.001.0001 ae0cf962-f685-4933-93d1-916defa5123d 9780262374736 9780262545648 The MIT Press 200 Cambridge open access |
| spellingShingle | Art history artificial intelligence machine learning formalism digital humanities connoisseurship image database authentication style thema EDItEUR::A The Arts::AG The Arts: treatments and subjects::AGA History of art thema EDItEUR::3 Time period qualifiers::3M c 1500 onwards to present day::3MN 19th century, c 1800 to c 1899 thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::A The Arts::AF The Arts: art forms::AFK Non-graphic and electronic art forms::AFKV Digital, video and new media arts Wasielewski, Amanda Computational Formalism |
| title | Computational Formalism |
| title_full | Computational Formalism |
| title_fullStr | Computational Formalism |
| title_full_unstemmed | Computational Formalism |
| title_short | Computational Formalism |
| title_sort | computational formalism |
| topic | Art history artificial intelligence machine learning formalism digital humanities connoisseurship image database authentication style thema EDItEUR::A The Arts::AG The Arts: treatments and subjects::AGA History of art thema EDItEUR::3 Time period qualifiers::3M c 1500 onwards to present day::3MN 19th century, c 1800 to c 1899 thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::A The Arts::AF The Arts: art forms::AFK Non-graphic and electronic art forms::AFKV Digital, video and new media arts |
| topic_facet | Art history artificial intelligence machine learning formalism digital humanities connoisseurship image database authentication style thema EDItEUR::A The Arts::AG The Arts: treatments and subjects::AGA History of art thema EDItEUR::3 Time period qualifiers::3M c 1500 onwards to present day::3MN 19th century, c 1800 to c 1899 thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::A The Arts::AF The Arts: art forms::AFK Non-graphic and electronic art forms::AFKV Digital, video and new media arts |
| url | ONIX_20230731_9780262374736_34 |
| work_keys_str_mv | AT wasielewskiamanda computationalformalism |