Mirror: Multimedia query processing in extensible databases

The mi Ror project investigates the implications of multimedia information retrieval on database design. We assume a modern extensible database system with extensions for feature based search techniques. The multimedia query processor has to bridge the gap between the user’s high level information need and the search techniques available in the database. We therefore propose an iterative query process using relevance feedback. The query processor identifies which of the available representations are most promising for answering the query. In addition, it combines evidence from different sources. Our multimedia retrieval model is a generalization of a well-known text retrieval model. We discuss our prototype implementation of this model, based on Bayesian reasoning over a concept space of automatically generated clusters. The experimentation platform uses structural object-orientation to model the data and its meta-data flexibly, without compromising efficiency and scalability. We illustrate our approach with some first experiments with text and music retrieval.

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