Determining image age with rank-consistent ordinal classification and object-centered ensemble

A significant number of old photographs including ones that are posted online do not contain the information of the date at which they were taken, or this information needs to be verified. Many of such pictures are either scanned analog photographs or photographs taken using a digital camera with incorrect settings. Estimating the date of such pictures is useful for enhancing data quality and its consistency, improving information retrieval and for other related applications. In this study, we propose a novel approach for automatic estimation of the shooting dates of photographs based on a rank-consistent ordinal classification method for neural networks. We also introduce an ensemble approach that involves object segmentation. We conclude that assuring the rank consistency in the ordinal classification as well as combining models trained on segmented objects improve the results of the age determination task.