Download NOE Proposal MUSCLE

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.