Visual Content Analysis and Linked Data for Automatic Enrichment of Architecture-Related ImagesMager, T., Khademi, S., Siebes, R., Hein, C., Boer, V. D. & Gemert, J. V., 2020, Digital Cultural Heritage. Kremers, H. (ed.). Springer International Publishing AG, p. 279-293 15 p.
Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review
Built form dominates the urban space where most people live and work and provides a visual reflection of the local, regional and global esthetical, social, cultural, technological and economic factors and values. Street-view images and historical photo archives are therefore an invaluable source for sociological or historical study; however, they often lack metadata to start any comparative analysis. Date and location are two basic annotations often missing from historical images. Depending on the research question other annotations might be useful, that either could be visually derived (e.g. the number or age of cars, the fashion people wear, the amount of street decay) or extracted from other data sources (e.g. crime statistics for the neighborhood where the picture was taken). Recent advances in automatic visual analysis and the increasing amount of linked open data triggered the research described in this paper. We provide an overview of the current status of automated image analysis and linked data technology and present a case study and methodology to automatically enrich a large database of historical images of buildings in the city of Amsterdam.
|Title of host publication||Digital Cultural Heritage|
|Publisher||Springer International Publishing AG|
|Number of pages||15|
|Publication status||Published - 2020|