K-Space

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Full title

Knowledge Space of Semantic Inference for Automatic Annotation and Retrieval of Multimedia Content

Project website

Duration of project

36 months, starting from Jan. 2006

Funding source

6th European Frame Programme (EU FP6)

Funding number

EU FP6 contract nb: FP6 – 027026

Description

K-Space integrated leading European research teams to create a Network of Excellence in semantic inference for semi-automatic annotation and retrieval of multimedia content. The aim was to narrow the gap between content descriptors that can be computed automatically by current machines and algorithms, and the richness and subjectivity of semantics in high-level human interpretations of audiovisual media: The Semantic Gap. The main objectives of K-Space are:

  • To bring together leading European research teams to create critical mass for innovation of currently highly fragmented research groups addressing semantic inference for semi-automatic annotation and retrieval of multimedia content.
  • To build an open and expandable framework for collaborative research on knowledge acquisition based on system made up of flexible, modular and interconnected technology.
  • To disseminate the technical developments of the network across the broad research community.
  • To boost technology transfer to industry, influence and contribute to related knowledge-based multimedia standardisation activities.

Research activities

The joint research activities of the network were aimed at convergence and resources optimization by exploiting important multidisciplinary aspects of multimedia knowledge extraction. This was achieved by linking research efforts over the following three research clusters underpinning the K-Space framework.

  • Content-based multimedia analysis: Tools and methodologies for low-level signal processing, object segmentation, audio processing, text analysis, and audiovisual content structuring and description.
  • Knowledge extraction: Building of a multimedia ontology infrastructure, knowledge acquisition from multimedia content, knowledge-assisted multimedia analysis, context based multimedia mining and intelligent exploitation of user relevance feedback.
  • Semantic multimedia: knowledge representation for multimedia, distributed semantic management of multimedia data, semantics-based interaction with multimedia and multimodal media analysis.