Read more
Informationen zum Autor Amanda Wasielewski Klappentext "Computational Formalism investigates examples of art historical analysis in the fields of computer and information sciences, and frames this research in the context of art historiography. The use of machine learning to analyze art images has ushered in a renewed interest in formalism in art history, but these new techniques create new critical challenges for the field"-- Zusammenfassung 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” to describe 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. Inhaltsverzeichnis Series Foreword ix Acknowledgments xi Introduction: Return to Form 1 Machine Learning and Computer Vision 3 The New Science Wars 11 Digital Art History 16 Objectivity and Cultural Studies 22 Art History and Objectivity 25 Computational Formalism 30 Questions of Style 34 1 The Shape of Data 39 Digitization and Dataset Creation 42 The Semantic Gap 49 Artificial ArtHistorian 51 Image Selection 60 Image Categorization 67 Stylistic Determinism 75 Style Unsupervised 79 Stylistic Devices 84 2 Deep Connoisseurship 87 Cat, Dog, or Virgin Mary? 92 Value, Fame, and the Artist's Hand 95 Opening the Black Box 101 The Business of Authenticity 107 Next-Level Forgeries and Fakes 115 An Artificial Artist? 119 Poor Images 124 3 Conclusion: Man, Machine, Metaphor 127 The Rise of the Humanities Lab 133 Foreign Metaphors as Interdisciplinary Tool 135 Appendix: Classification by Artistic Style, Publications in Computer Science, 2005-2021, Including the Development and Utilization of Fine Art Datasets 139 Notes 145 Index 177...
