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NOE Proposal MUSCLE Multimedia Understanding through Semantics, Computation and Learning Vision Efficient multimedia content mining presupposes access to information using semantic-based metadata. A large number of Semantic Web initiatives therefore focus on the problems of standardization and interpretation of such metadata. However, in order to use semantically rich metadata, we first need to be able to generate them, and generate them automatically! Failing to do so means that developers for the Semantic Web will still be forced to laboriously annotate huge quantities of data before they can actually channel them into their specific applications, thus creating a bottleneck that jeopardizes the whole undertaking. It is our contention that enriching multimedia databases with additional layers of automatically generated semantic metadata, as well as with artificial intelligence to reason about these (meta)data, is the only conceivable way to mine for multimedia content, and this is exactly what MUSCLE aims to accomplish. More specifically, MUSCLE aims at creating a research network to foster close collaboration between researchers in multimedia data mining on the one hand, and statistical and machine learning on the other, to harness the full potential of machine learning for the automatic generation of robust and semantically rich metadata for multimedia documents. This will allow users to move away from labor-intensive case-by-case modelling of individual applications, and take full advantage of generic adaptive and self-learning solutions that need minimal supervision. Notice that, since MUSCLE actually generates the semantic metadata that will populate ontologies, schemata etc, it is a highly complementary key enabler to other Semantic Web proposals (such as KNOWLEDGE WEB). Moreover, machine learning and inferencing will not be restricted to rule- or language-based approaches (as is the case for REWERSE) but explicitly include sophisticated statistical and numerical techniques which can handle uncertainty within an appropriate probabilistic setting and are therefore of crucial importance for the reliable interpretation and visualisation of multimedia content. Specific MUSCLE strengths Organizational: The MUSCLE consortium comprises 41 research groups and is headed by ERCIM which has extensive experience with, as well as strong commitment to, Europe-wide integration. Many of the tools for structural integration called for by the EU (e.g. working groups, exchange and mobility of researchers) have already been successfully implemented by ERCIM. Using this existing infrastructure will give MUSCLE a headstart over other Networks. Moreover, ERCIM's involvement guarantees continuation beyond the period of EU support. Scientific: To stimulate cohesion and genuine integration, the NOE has set itself two "grand challenges", i.e. ambitious internal research projects in multimedia content mining, involving the whole spectrum of expertise represented within the consortium. As such, they require intensive collaboration of all groups and therefore guarantee actual and durable integration. Dissemination and Exchange: ERCIM is the European host of the W3C consortium which will ensure the NOE's impact on Semantic Web initiatives. Part of the NOE funding will be used for industrial liaison and the creation of fellowships (open to international competition beyond the NOE boundaries) to ensure spreading of excellence and uptake by relevant end-users. Moreover, the NOE organisational structure allows for the influx of additional expertise or partners wherever necessary. Furthermore, MUSCLE enthusiastically welcomes collaboration with complementary proposals in order to maximize impact.