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Multimedia understanding through semantics, computation and learning

Funded under: FP6-IST

Project reference: Grant Agreement n. 507752

MUSCLE aims at creating and supporting a pan-European Network of Excellence to foster close collaboration between research groups in multimedia data-mining and machine learning in order to make breakthrough progress towards the following objectives: MUSCLE is an EC-sponsored Network of Excellence that aims at establishing and fostering closer collaboration between research groups in multimedia datamining and machine learning.

The Network integrates the expertise of over forty research groups working on image and video processing, speech and text analysis, statistics and machine learning.

The goal is to explore the full potential of statistical learning and cross-modal interaction for the (semi-)automatic generation of robust meta-data with high semantic value for multimedia documents.

In particular, MUSCLE researchers are developing software tools and research strategies to enable users to move away from labor-intensive case-by-case modelling of individual applications, and allow them to take full advantage of generic adaptive and self-learning solutions that need minimal supervision.


  • Harnessing the full potential of machine learning and cross-modal interaction for the (semi-) automatic generation of meta-data with high semantic content for multimedia documents.
  • 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.

Improving interoperability and exchangeability of heterogeneous and distributed metadata 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.



Role in the project

The Signals & Images Lab is working on many aspects of multimedia content management, such as signal acquisition and processing, image understanding and artificial vision, high performance and distributed computing, real-time data collection and transmission. The general goal of the Lab is to increase both theoretical and applied knowledge in these fields.