This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods.

翻译：本文介绍了从档案来源对历史照片进行日期估计的新方法,主要贡献是将日期估计作为一项检索任务,如果给一个查询,检索到的图像按估计日期相似性排列。越近于其嵌入的表示方式,就越近于其日期。与设计神经网络以学习一个分类器或倒退器的传统模型相反,我们根据NDCG等级衡量标准提出了一个学习目标。我们实验性地评估了该方法在两种不同任务中的绩效:日期估计和对日期敏感的图像检索,利用DEW公共数据库,克服基线方法。