In this paper, we demonstrate the benefits of using state-of-the-art machine learning methods in the analysis of historical photo archives. Specifically, we analyze prominent Finnish World War II photographers, who have captured high numbers of photographs in the publicly available SA photo archive, which contains 160,000 photographs from Finnish Winter, Continuation, and Lapland Wars captures in 1939-1945. We were able to find some special characteristics for different photographers in terms of their typical photo content and photo types (e.g., close-ups vs. overview images, number of people). Furthermore, we managed to train a neural network that can successfully recognize the photographer from some of the photos, which shows that such photos are indeed characteristic for certain photographers. We further analyze the similarities and differences between the photographers using the features extracted from the photographer classifier network. All the extracted information will help historical and societal studies over the photo archive.
翻译:在本文中,我们展示了在分析历史照片档案时使用最先进的机器学习方法的好处。具体地说,我们分析了著名的芬兰二战摄影师,他们在可公开查阅的SA照片档案中收集了大量照片,其中含有1939-1945年芬兰冬季、继续和拉普兰战争拍摄的160,000张照片。我们发现,不同摄影师的典型照片内容和照片类型(例如特写和概览图像、人数)方面有一些特殊特征。此外,我们设法训练了一个神经网络,能够成功地认出一些照片中的摄影师,这显示这些照片确实是某些摄影师的特征。我们利用摄影师分类网络所提取的特征进一步分析了摄影师之间的相似和差异。所有提取的信息将有助于对照片档案进行历史和社会研究。