Multimedia knowledge representation for automatic annotation of broadcast TV archives

Multimedia content classification and retrieval are indispensable tools in the current convergence of audiovisual entertainment and information media. Thanks to the development of broadband networks, every consumer will have digital video programmes available on-line as well as through the traditional distribution channels. In this scenario, since the early '90s, the most important TV broadcasters in Europe have started projects whose aim was to achieve preservation, restoration and automatic documentation of their audiovisual archives, In particular, the association of low-level multimedia features to knowledge and semantics for the purpose of automatic classification of multimedia archives is currently the target of many researchers in both academic and IT industrial communities, This paper describes our research direction, which is focusing on three points: (a) We first introduce a new taxonomy for classification of broadcast digital archives based on a novel theoretical approach. The advantage of this taxonomy is that it can provide an unambiguous representation of multi-media informative content from the relevant points of view to the broadcasters community. (b) We secondly present a multilayer multimedia database model to represent both structure and content of multimedia objects. (c) We further propose a framework architecture for building a Multimedia Fuzzy Annotation System (MFAS), and a description of our experimental plan.