10 January 2003 Supervised multimedia categorization
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Abstract
Static multimedia on the Web can already be hardly structured manually. Although unavoidable and necessary, manual annotation of dynamic multimedia becomes even less feasible when multimedia quickly changes in complexity, i.e. in volume, modality, and usage context. The latter context could be set by learning or other purposes of the multimedia material. This multimedia dynamics calls for categorisation systems that index, query and retrieve multimedia objects on the fly in a similar way as a human expert would. We present and demonstrate such a supervised dynamic multimedia object categorisation system. Our categorisation system comes about by continuously gauging it to a group of human experts who annotate raw multimedia for a certain domain ontology given a usage context. Thus effectively our system learns the categorisation behaviour of human experts. By inducing supervised multi-modal content and context-dependent potentials our categorisation system associates field strengths of raw dynamic multimedia object categorisations with those human experts would assign. After a sufficient long period of supervised machine learning we arrive at automated robust and discriminative multimedia categorisation. We demonstrate the usefulness and effectiveness of our multimedia categorisation system in retrieving semantically meaningful soccer-video fragments, in particular by taking advantage of multimodal and domain specific information and knowledge supplied by human experts.
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Frank Aldershoff, Frank Aldershoff, Alfons H. Salden, Alfons H. Salden, Sorin Marcel Iacob, Sorin Marcel Iacob, Masja Kempen, Masja Kempen, } "Supervised multimedia categorization", Proc. SPIE 5021, Storage and Retrieval for Media Databases 2003, (10 January 2003); doi: 10.1117/12.476242; https://doi.org/10.1117/12.476242
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