Jonathan Dentler, EyCon Postdoctoral Research Fellow
The Early Conflict Photography 1890—1918 and Visual AI (EyCon) project is partnered with several UK and French archives and museums, including La Contemporaine, which notably holds the fonds des albums Valois, which contain the photographs produced by the Section photographique de l’armée (SPA) during World War One. Using both Application Programming Interfaces (APIs) for already-digitized collections, as well as scanning of collections that have not already been digitized, Eycon has assembled a very large corpus of images, and is now advancing to the computing stage. Computation involves training a neural network to recognize various features of the images in the corpus, with the aim of enriching their associated metadata, increasing their navigability, and making them more useful for researchers and the public. This short post will explain the project’s goals, with a focus on the fonds Valois. In particular, it will consider how machine learning holds the potential for new contributions to existing scholarship on photography, personal presentation, and emotion, as well as how the SPA attempted to shape the image of the war for domestic and international audiences.
Created in 1915 in order to counter German photo propaganda, the SPA was a collaboration between the École des Beaux-Arts, the Ministry of War, the Ministry of Public Instruction, and the Ministry of Foreign Affairs, which mobilized professional photographers to document the war for the purposes of propaganda as well as the creation of national patrimony and future public memory of the conflict. While the Minister of War charged the photographers with their general mission and assigned them to sections of the war front and the home front, the ministry of public instruction and the Beaux Arts took charge of logistics and funding, and the Minister of Foreign Affairs diffused the photos to the press in allied and neutral countries. The effort was organized on an industrial scale, and generated a vast number of photographic prints in a wide variety of formats (Guillot, 2010).[1] Digitization of this collection has allowed for a whole new set of uses by researchers and the public (Maurel and Morvan, 2016), and machine learning promises to build on this success in important ways.
Having assembled the EyCon database, the research team is now applying a set of deep learning tools to segment pages into images and associated text, estimate human pose and gesture, and to detect objects in the images. These techniques can be used to enrich searchable metadata for digitized archives, dramatically enhancing its navigability for researchers and widening the parameters by which one can search for a given set of images. While the fonds Valois already has quite detailed associated metadata for the photographs in its albums—including information such as date, caption, and location—the ability to automatically recognize objects and poses in the photo and produce new metadata can supplement this information, substantially enhancing the collection’s usefulness for researchers and the public.
Indeed, the fonds Valois’ relatively replete metadata makes it a vital collection for the EyCon project’s wider goals, because it allows us to assemble a training dataset for the deep learning network. In order to teach the network to recognize human poses such as “standing,” “sitting,” “lying down,” or “walking,” it is necessary to assemble at least ten thousand images tagged with this information as a training set. After computation, the OpenPose algorithm should be able to recognize such poses in the images without the assistance of preexisting metadata (Cao et. al., 2019). The fonds Valois’ existing searchable metadata allows us to find all the images that contain these terms in the captions, and automatically compile them into a training set. Inputting “assis” into Argonaute, for example, might return image VAL 413/091, which was taken on April 21, 1918 and depicts “French and British artillerymen sitting on a 75mm canon.” This image, along with all the others with “assis” in their metadata can be compiled into a training set.
Researchers have used the fonds Valois to pose questions about topics such as the relationship between photography, the war experience, emotion, and masculinity (Pichel 2021). However, scholars are limited by time and labor constraints to working with a fairly limited set of photos, often picking one battle or album (Pichel, 2012). Researchers who have applied quantitative methods to visual sources tend to confine their sample sizes to between four hundred and one thousand images (Männistö et. al., 2022). Information about pose can be extremely useful to the researcher asking questions about how experience of war, as well as the SPA’s official photographic representation of it, changed over the course of the conflict. In general, sitting connotes a more relaxed photographic model, and indeed in this instance the men are smiling and smoking. If a researcher could establish a change in the relative prevalence of photos displaying sitting soldiers over the course of the war, they could make an argument about changing photographic practices, which may have shifted from a stiffer and more official relationship to the model to a more relaxed attitude, emphasizing confidence, ease, and camaraderie among enlisted men (Marly, 2018).
Pose estimation will help to make such arguments by allowing the researcher to work at a quantitative scale that would be otherwise impossible, as well as by alerting the researcher to relevant photos for which existing metadata would not have been sufficient. Take, for example, VAL 320/045 dated January 15, 1917 and taken in the area of Alsace.
The photo shows a group of five tirailleurs sénégalais sitting down to a meal, and one soldier standing. Two of the soldiers are smiling, conveying a relaxed air of camaraderie behind the front lines. Yet the caption—“Billet of the Senegalese. The soup of the infantrymen (tirailleurs)”—contains no information on pose or facial expression, and would thus not come up in a search for “sitting” in Argonaute. EyCon will apply machine learning tools such as pose estimation and expression recognition to enrich such images’ associated metadata, opening up whole new avenues of research into emotion and photographic practice during World War One.
With a much higher degree of precision than is afforded even by the relatively replete metadata of the fonds Valois, researchers could ask questions such as “What is the relative prevalence of sitting vs. standing photos in representations of soldiers over the course of the war? How do such iconographic trends change over time?” These choices by SPA photographers could indicate a shift from early optimism to later disillusion and exhaustion, or a more relaxed stylistic approach. Or, they might point to changing propaganda strategies that shifted from a stricter adherence to an iconography of heroism to a limited admission of the hardship, sacrifice, and day-to-day routines and habitus of soldiers at the front. By calculating the distance of the subjects from the photographer and how it changed over time, researchers could study the relative prevalence of close ups, medium shots that show groups close enough to depict their actions, or more distant landscape shots. Close up shots tend to provoke identification with the subject, and a different emotional response in the viewer (Chumachenko et. al., 2020). Alternatively, using object detection a researcher could find photos that include smoking or certain types of military equipment and uniform, useful information for studies of material culture. The possibilities are nearly limitless.
Machine learning has the potential to reduce the time and labor required for annotating photographs, compiling large numbers of images for quantitative study around a wide variety of variables, and enables study of large image collections on a new scale. Such tools will shift the labor of quantitative visual analysis away from compiling data and toward the higher-level work of interpretation. These techniques are particularly interesting for the photographic archive, and will allow researchers to see this medium and its history anew; while traditional art historical methods were developed in the context of a relatively limited number of artists and works, machine learning allows researchers to take the industrial scale of photographic production into account for the first time.
References:
Cao, Zhe, Gines Hidalgo, Tomas Simon, Shih-En Wei, and Yaser Sheikh. “OpenPose: Realtime Multiperson 2D Pose Estimation Using Part Affinity Fields.” IEEE Transactions on Pattern Analysis and Machine Intelligence 43.1, pp. 172-186.
Chumachenko, Kateryna, Anssi Männistö, Alexandros Iosifidis, and Jenni Raitoharju. “Machine Learning Based Analysis of Finnish World War II Photographers.” IEEE Access, vol. 8, pp. 144184-144196, 2020.
Guillot, Hélène. “La section photographique de l’armée et la Grande Guerre: De la creation en 1915 à la non-dissolution.” Revue historique des armées, 258, 2010, pp. 110-117.
Männistö, Anssi, Mert Seker, Alexandros Iosifidis, and Jenni Raitoharju.“Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives.” arXiv preprint arXiv:2204.02149 (2022).
Marly, Mathieu. “L’armée rend-elle virile ? Réflexions sur le “modèle militaro-virile à la fin du IXe siècle.” Clio. Femmes, Genre, Histoire, 47, 2018, pp. 229-247.
Maurel, Lionel, Josselin Morvan. “Les circulations sur le web d’un corpus numérisé: l’exemple des albums Valois,” Rapport final du projet : “Le devenir du patrimoine numérisé en ligne: l’exemple de la grande Guerre.” 2016.
Pichel, Beatriz. “Making Sense of Verdun: Photography and Emotions During the First World War in France,” in On Suffering: An Interdisciplinary Dialogue on Narrative and Suffering. London: Brill, 2012.
Pichel, Beatriz. Picturing the Western Front: Photography, Practices, and Experiences in First World War France. Manchester: Manchester University Press, 2021.
OpenEdition vous propose de citer ce billet de la manière suivante :
Marina Giardinetti (16 décembre 2022). Applying Pose Recognition to the World War One Valois Albums: Some AI Avenues for Photography History. EyCon. Consulté le 23 mai 2025 à l’adresse https://doi.org/10.58079/12d6e