Deep learning as a tool for early cinema analysis

Visual Cultural Heritage has extensively been explored using multimedia methods, but has so far been limited to still images. In particular, Early Cinema has hardly been explored. We analyze the Desmet collection, a recently digitized collection of early cinema (1907-1916), in the context of intertitles. Intertitles played an important role in silent movies in order to convey the main narratives, and split the film into semantically meaningful segments. We first build several classifiers to detect these intertitles, and evaluate it on a gold standard collection annotated by an expert. We illustrate the usefulness of using Deep Learning methods to extract semantic features to analyze the role of intertitles in early cinema. Furthermore, we attempt to structure and map the narrative progression of a film with respect to the locations at which shots were filmed.