Re-ranking Image Retrieval in Challenging Geographical Iconographic Heritage Collections

As the number of digitized geographic iconographic heritage collections increases, their global use is under-exploited by their lack of structure at large scale, which does not facilitate their access nor their understanding. Using automatic image retrieval methods appears to be the solution to bring structure by building links between contents, within and between collections. This paper presents an overview of methods for image retrieval applied to geographic iconographic heritage collections, both from the perspectives of image content description and of post-processing re-ranking. The article evaluates features and methods to identify their efficiency when faced with a challenging dataset. Moreover, new re-ranking approaches exploiting structuring information (scene geometry, metadata) are proposed to improve retrieval without having to adapt image descriptors to the specific data (retraining, fine-tuning, etc.) for every new specific collection.