Abstract
Digital library applications based on huge amounts of digital video data require efficient browsing and searching mechanisms for the extraction of relevant information. To avoid information overload, a browsing system needs to preselect shots of interest from the database in a user-adequate manner. A retrieval engine for video browsing is proposed that offers conceptual, content-based access to videos. It calculates relevance values for the results of a conceptual query by feature aggregation on video shot granularity. This engine is embedded in a browsing system architecture which was extended with an intelligent client buffer strategy and admission control mechanism aiming for browsing specific requirements. Thus, we support continuous presentation of time-dependent media and reduce startup latency.
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