Multimedia Understanding through Semantics, Computation and Learning

Multimedia understanding, semantic web, machine learning, data mining


  • MUSCLE aims at creating and supporting a pan-European Network of Excellence to foster close collaboration between research groups in multimedia datamining on the one hand and machine learning on the other in order to make breakthrough progress towards the following objectives:
  • Harnessing the full potential of machine learning and cross-modal interaction for the (semi-) aut omatic generation of metadata with high semantic content for multimedia documents;
  • Applying machine learning for the creation of expressive, context-aware, self-learning, and human-centered interfaces that will be able to effectively assist users in the exploration of complex and rich multimedia content;
  • Improving interoperability and exchangeability of heterogeneous and distributed (meta)data by enabling data descriptions of high semantic content (e.g. ontologies, MPEG7 and XML schemata) and inference schemes that can reason about these at the appropriate levels.
  • Through dissemination, training and industrial liaison, contribute to the distribution and uptake of the technology by relevant end-users such as industry, education, and the service sector. In particular, close interactions with other IP's and NOE's in this and related activity fields are planned.
  • Through accomplishing the above, MUSCLE will facilitate the broad and democratic access to information and knowledge for all European citizens (e.g. e-Education, enriched cultural heritage).

ERCIM, CWI, Advanced Computer Vision, Austria, Aristotle University of Thessaloniki, Greece, Albert-Ludwigs-Universitat Freiburg, Seibersdorf Research, ARMINES, Bilkent University, Cambridge University UK, Commissariat a l'Energie Atomique, CNRS (Centre National de la Recherche Scientifique, Institute of Information Theory and Automation, Czech Republic), Ecole Nationale Supérieure des Telecommunications, France, Ecole Nationale Supérieure de l’Electronique et de ses Applications, France, FORTH, France Telecom R&D, Institut fur Bildverarbeitung und angewandte Informatik e.V, Germany, INRIA-Ariana, INRIA-Imedia, INRIA-PAROLE, INRIA-Texmex, INRIA-Vista, CNR-ISTI, KTH (Royal Institute of Technology, Sweden), LTU Technologie, National Technical University of Athens, Greece, MTA-SZTAKI (Computer and Automation Research Institute of the Hungarian Academy of Sciences), TAU-Speech (Tel Aviv University, Israel), TAU-Visual (Tel Aviv University, Israel), Trinity College Dublin. Ireland), Technion-ML (Israel Institute of Technology, Israel), Technion-MM (Israel Institute of Technology, Israel), Technical University of Crete, Greece, Graz University of Technology), TU Vienna-IFS (Vienna University of Technology, Austria), TU Vienna-PRIP (Vienna University of Technology, Austria), University College London, UK), University of Surrey, UK), UPC (Universitat Politecnica de Catalunya), University of Ulster, UK), University of Amsterdam, Netherlands, VTT.

48 months. 1 March 2004 - 29 February 2008

ERCIM's role:
Project co-ordinator

Total budget:
6 900 000 Euro

Funding Agencies:
European Commission FP6
Unit E2 : Knowledge Management and Content Creation

MUSCLE home page: