Automatic identification and classification of portraits in a corpus of historical photographs

Abstract

There have been recent calls for an increased focus on the application of computer vision to the study and curation of digitised cultural heritage materials. In this short paper, we present an approach to bridge the gap between existing algorithms and humanistically driven annotations through a case study in which we create an algorithm to detect and and classify portrait photography. We apply this method to a collection of about 40,000 photographs and present a preliminary analysis of the constructed data. The work is part of the larger ongoing study that applies computer vision to the computational analysis of over a million U.S. documentary photographs from the early twentieth century.

Document Type

Article

Publication Date

12-2022

Publisher Statement

© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

CEUR Workshop Proceedings (CEUR-WS.org)

Share

COinS