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<p>This article advances a method to analyze a large corpus of historical photographs using artificial intelligence tools and data modeling. This research was conducted within the framework of the EyCon (Early Conflict Photography 1890-1918 and Visual AI) and HighVision projects, which aim at leveraging the power of digital tools, exploiting both visual and textual information, to investigate the development of war photography at the turn of the 20th century. To do so, one of the objectives of the project was to develop a method to extract robust features and to overcome the challenges posed by the halftone printing techniques, the most common way to reproduce photographs in daily newspapers, periodicals and books at the time. By combining visual and textual similarity measures, the proposed approach enables the identification of significant subsets of similarity within the dataset. The findings from this research hold important implications for the broader field of image analysis and provide insights into the unique characteristics and complexities of historical visual data. This work contributes to the advancement of computer vision techniques in the analysis of historical photographic collections, opening up new avenues for research in visual AI and archival studies.</p>

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Computer vision and halftone visual culture: improving similarity search for historical photographs

  • 期刊论文

  • 发表日期：

    2025年4月26日

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  • Mohamed Salim Aissi,
  • Marina Giardinetti,
  • Isabelle Bloch,
  • Julien Schuh,
  • Daniel Foliard

摘要

This article advances a method to analyze a large corpus of historical photographs using artificial intelligence tools and data modeling. This research was conducted within the framework of the EyCon (Early Conflict Photography 1890-1918 and Visual AI) and HighVision projects, which aim at leveraging the power of digital tools, exploiting both visual and textual information, to investigate the development of war photography at the turn of the 20th century. To do so, one of the objectives of the project was to develop a method to extract robust features and to overcome the challenges posed by the halftone printing techniques, the most common way to reproduce photographs in daily newspapers, periodicals and books at the time. By combining visual and textual similarity measures, the proposed approach enables the identification of significant subsets of similarity within the dataset. The findings from this research hold important implications for the broader field of image analysis and provide insights into the unique characteristics and complexities of historical visual data. This work contributes to the advancement of computer vision techniques in the analysis of historical photographic collections, opening up new avenues for research in visual AI and archival studies.