A Framework for Visual Search in Broadcast archives

In today’s digital age, the ability to access, analyze and (re)use ever-growing amounts of data is a strategic asset for the broadcasting and media industry. Despite the growing interest around new technologies, archive’s search and retrieval operations are still usually done by means of text-based search over tags and metadata of manually pre-annotated material. This is particularly true because of its reliability and the broad availability of powerful full-text search platforms. However, this approach still does not completely meet the requirements that a search over huge multimedia archives poses, such as the need for semantic-driven indexing and retrieval, or the possibility to access contents based on visual features. In this paper, we describe a framework currently under development in Rai that enables visual search over the company’s archive, which includes still images as well as annotated broadcast contents and raw footages, totaling over 1.5 million hours of video material. The current architecture’s core is based on LIRe (Lucene Image Retrieval), an open source Java Library for content-based image retrieval, and Apache Solr, an enterprise full-text search platform. Possible extensions of the framework to include new technologies such as deep learning or semantic learning are also discussed.