Recognizing Scene Categories of Historical Postcards

The recognition of visual scene categories is a challenging issue in computer vision. It has many applications like organizing and tagging private or public photo collections. While most approaches are focused on web image collections, some of the largest unorganized image collections are historical images from archives and museums. In this paper the problem of recognizing categories in historical images is considered. More specifically, a new dataset is presented that addresses the analysis of a challenging collection of postcards from the period of World War I delivered by the German military postal service. The categorization of these postcards is of greater interest for historians in order to gain insights about the society during these years. For computer vision research the postcards pose various new challenges such as high degradations, varying visual domains like sketches, photographs or colorization and incorrect orientations due to an image in the image problem. The incorrect orientation is addressed by a pre-processing step that classifies the images into portrait or landscapes. In order to cope with the different visual domains an ensemble that incorporates global feature representations and features that are derived from detection results is used. The experiments on a development set and a large unexplored test set show that the proposed methods allow for improving the recognition on the historical postcards compared to a Bag-of-Features based scene categorization.