Importance

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Abstract

Meta-data (tags) for an audiovisual file can be generated by producing an initial estimate of the tags and then revising the estimate (notably to expand it and/or render it more precise) based on the assumption that the relationships which hold between the different tags for a set of manually-tagged training examples will also hold for the tags of the input file now being tagged. A fully-automatic method and system is a hybrid between signal-based and machine-learning approaches, because the initial tag estimate is based on the physical properties of the signal representing the audiovisual file. The initial tag estimate may be produced by inferring that the input content will have the same tags as those files of the same kind, in the training database, which have a global similarity to the input audiovisual file in terms of signal properties.

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First Claim