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-) 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-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.
• Ensure durable integration and collaboration through the creation of a virtual lab that facilitates the easy and immediate access to people, data and ideas.
• 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 IPs and NoEs in this and related activity fields are planned.
• Through accomplishing the above, facilitate the broad and democratic (i.e. obviating the need for special expertise) access to information and knowledge for all European citizens (e.g. e-Education, enriched cultural heritage).

Network cohesion and integration based on two Grand Challenges
To stimulate cohesion, the NoE will set itself two grand challenges.

• Grand Challenge #1: Natural high-level interaction with multimedia databases In this vision it is possible to query a multimedia database at a high semantic level. Think Ask Jeeves for multimedia content, where one can address a search engine using natural language and it will take appropriate action, or at least ask intelligent, clarifying questions. This is an extremely challenging problem and will involve a wide range of techniques: natural language processing, interfacing technology, learning and inferencing, merging of different modalities, federation of complex meta-data, appropriate representation and interfaces, etc.

• Grand Challenge #2: Detecting and interpreting humans and human behaviour in videos Many important applications of multimedia data mining revolve around the detection and interpretation of human behaviour. Applications are legion: surveillance and intrusion detection, face recognition and registration of emotion or affect, automatic analysis of sports videos and movies, etc. Again, success will depend heavily on the integration and interpretation of various modalities such as vision, audio and speech.