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Research & Integration


WP2: Evaluation, Integration and Standards
WP3: Visual Content Indexing
WP4: Content Description for Audio, Speech and Text
WP5: Multimodal Processing and Interaction
WP6: Machine Learning and Computation Applied to Multimedia
WP7: Dissemination towards Industry

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WP1: Coordination

  • Scientific Coordination: Eric Pauwels (CWI)
  • Administrative Coordination: Remi Ronchaud (ERCIM)

WP2: Building the Virtual Lab

  • WP Leader: Eric Pauwels (CWI)
  • The primary objective of the NoE Virtual Lab is to facilitate collaboration and integration by providing an electronic portal to the knowledge available in the MUSCLE network and to encourage the sharing of expertise and ideas among the members of the network.
  • Web site: n/a     

WP3: Benchmarking

  • WP Leader: Allan Hanbury (PRIP TU-Vienna)
  • During the lifetime of the MUSCLE NoE, a large number of algorithms and complete systems for use in the automatic extraction of semantic information from multimedia data will be developed. This workpackage aims to develop objective methods for comparing these algorithms, and to encourage the use of these methods.
  • Web site    

WP4: Dissemination and Training

  • WP Leader: Panos Trahanias (FORTH)
  • Expertise accumulated in the Network will be disseminated through contacts with industry and academia, Fellowships, summerschools and contacts with other Networks and Integrated Projects.
  • Web site     

WP5: Content-based Description

  • WP Leader: Nozha Boujemaa (INRIA-IMEDIA)
  • This workpackage addresses the problem of designing various content-based descriptors for MM modalities, such as still images and video, speech and audio, text and natural language.
  • Web site

WP6: Cross-Modal Integration for Multimedia Content

  • WP Leader: Petros Maragos (NTUA-ICCS)
  • This work package addresses research on the theory and applications of multimedia analysis approaches that improve robustness and performance through cross-modal interaction and/or integration.
  • Web site    

WP7: Computation Intensive Methods                  

  • WP Leader: Simon Wilson (TCD)
  • The challenge for this WP is to develop computational methods that successfully implement solutions to problems in MM-understanding. These solutions contain large and/or complex data, and require complex modeling.
  • Web site : n/a

WP8: Machine Learning for Multimedia Content

  • WP Leader: Padraig Cunningham (TCD)
  • This workpackage aims to explore different ways in which machine learning (ML) can contribute to the automatic categorization, marking up and exploration of multimedia data.
  • Web site: n/a    

WP9: Representation and Communication of Data and Meta-Data

  • WP Leader: Ovidio Salvetti (CNR)
  • Enabling interaction and exchange of meta-data emanating from different MM modalities requires standardization of data and meta-data formats.
  • Web site  

WP10: Human-Computer Interface for Multimedia Retrieval                  

  • WP Leader: Alexandros Potamianos (TSI-TUC)
  • Multimedia information retrieval via an interactive human-computer interface is a complex task that requires feedback from the user and a complex negotiation between the user and the machine. We propose to research, design and build natural and efficient human-computer interfaces for performing multimedia information retrieval tasks that allow for negotiation (dialogue) between the user and the system.
  • Web site

WP11: Integration and Grand Challenges

  • WP Leader: Enis Cetin (Bilkent Univ)
  • The main objective of this WP will be to stimulate integration and collaboration between various partners by conducting research related to the Grand Challenges that have been put forward by Consortium. The two Grand Challenges of the Network are
         1. Natural high-level interaction with multimedia databases, and
         2. Detecting and interpreting humans and human behaviour in videos.