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arXiv 2109.01732cs.CVcs.DLcs.IR
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浏览Mise-en-Page:多民族期刊视觉布局的解释性机器学习方法

Navigating the Mise-en-Page: Interpretive Machine Learning Approaches to the Visual Layouts of Multi-Ethnic Periodicals

University of Washington(华盛顿大学);Pennsylvania State University(宾夕法尼亚州立大学)

Benjamin Charles Germain Lee, Joshua Ortiz Baco, Sarah H. Salter, Jim Casey

摘要:

本文提出了一种计算分析方法,该方法借鉴了机器学习、图书馆学和文学研究,以绘制19世纪末和20世纪初美国多民族报纸的视觉版面。这项工作背离了以前的报纸方法,专注于单个的文本和视觉内容。我们的方法结合了Chronicling America的MARC数据和Newspaper Navigator机器学习数据集来识别报纸页面布局的视觉模式。通过分析高维视觉相似性,我们的目标是更好地了解编辑如何通过他们的论文布局发言和抗议。

英文摘要:

This paper presents a computational method of analysis that draws from machine learning, library science, and literary studies to map the visual layouts of multi-ethnic newspapers from the late 19th and early 20th century United States. This work departs from prior approaches to newspapers that focus on individual pieces of textual and visual content. Our method combines Chronicling America's MARC data and the Newspaper Navigator machine learning dataset to identify the visual patterns of newspaper page layouts. By analyzing high-dimensional visual similarity, we aim to better understand how editors spoke and protested through the layout of their papers.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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