文章基本信息

标题：Transforming Information Into Knowledge: How Computational Methods Reshape Art History
本地全文：下载
作者：Sabine Lang ; Björn Ommer
期刊名称：DHQ
印刷版ISSN：1938-4122
出版年度：2021
卷号：15
期号：3
语种：English
出版社：Alliance of Digital Humanities
摘要：Current research in computer vision highlights the potential of using computational methods to analyze and access large datasets of real images and videos, performing tasks such as object detection or finding visual similarities. This essay describes how the application of these computational methods to digital art data transforms information inherent in images into new knowledge. We support this claim by presenting various research examples in the field of digital art history, which utilize computational methods for art analysis. We argue that in order to create new knowledge, we must involve transformative processes — from analog to digital data and digital to computational methods. Traditional methods used in art history to access datasets, link or edit images provide suggestions and validations for current practices, but are not sufficient role models for the processing of digital data because they were developed under varying technological conditions and standards and within a different historical context. Aby Warburg’s (1866-1929) Mnemosyne Atlas, which aimed to visualize visual continuities from antiquity to the Renaissance, is one method often cited in the context of digital humanities. We argue that the characteristics of digital data, for example being reproducible or modifiable, require innovative computer methods that do not have a direct analog counterpart. Our argument is based on the success of current projects and personal experience: the authors have expertise in computer vision and art history. They have created and applied computational methods to analyze art data and are thus able to identify shortcomings of traditional approaches and suggest possible solutions. Eventually this essay presents solutions, which demonstrate the great potential of computational methods for a data analysis: approaches enable an easy and explorative access to digital image collections through visualization techniques or an automatic object search. Computational methods establish links between thousands of images, thereby identifying the adaptation of specific motifs or styles by artists over time, or enable a conspicuous editing of images, thus providing new insights for art history unobtainable with analog methods.