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Multimedia understanding through semantics, computation and learning

Objectif

Due to the convergence of several strands of scientific and technological progress we are witnessing the emergence of unprecedented opportunities for the creation of a knowledge driven society. Indeed, databases are accruing large amounts of complex multimedia documents, networks allow fast and almost ubiquitous access to an abundance of resources and processors have the computational power to perform sophisticated and demanding algorithms.

However, progress is hampered by the sheer amount and diversity of the available data. As a consequence, access can only be efficient if based directly on content and semantics, the extraction and indexing of which is only feasible if achieved automatically. MUSCLE aims at creating and supporting a pan-European Network of Excellence to foster close collaboration between research groups in multimedia data mining on the one hand and machine learning on the other in order to make breakthrough progress towards the following objectives.(i) Harnessing the full potential of machine learning and cross-modal interaction for the (semi-)automatic generation of metadata with high semantic content for multimedia documents.(ii) Applying machine learning for the creation of expressive, context-aware, self-learning, and human centred interfaces that will be able to effectively assist users in the exploration of complex and rich multimedia content.(iii) Improving interoperability and exchangeability of heterogeneous and distributed (meta)data try enabling data descriptions of high semantic content (e.g. ontologies, MPEG7 and XML schemata) an conference schemes that can reason about these at the appropriate levels.(iv) Through dissemination, training and industrial liaison, contribute to the distribution and uptake the technology by relevant end-users such as industry, education, and the service sector.

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NoE - Network of Excellence

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GEIE ERCIM
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2004, ROUTE DES LUCIOLES SOPHIA ANTIPOLIS
06410 BIOT
France

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Dernière mise à jour: 10 Decembre 2007

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Permalink: https://cordis.europa.eu/project/id/507752/fr

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