Abstract Seminal: Additive Semantic Content for Multimedia Streams

Abstract

Technological advances such as higher network bandwidth and greater end-user computing power provide the basis for new types of media rich applications. As applications produce larger numbers of more diverse media streams, the content becomes too overwhelming to be useful in its raw form. The contribution of this work is the initial design of Seminal, a model that solves the problem of multimedia overload by enhancing multimedia streams with semantic information about their content and relationship. The goal of Seminal is to manually or automatically derive semantic meaning from a given set of media streams. When the media streams are presented, archived, or distributed between users, the semantics are used to filter the most relevant information from the entire information base. We have designed a digital classroom-based prototype to validate our assumption that semantic information can be used to allow users to interact in a media rich environment.