Advanced Interaction:
Multimedia Annotation
Investigators: Bob Hendley, Russell Beale, Chris Creed, Pete Lonsdale
CASAM introduces the notion of computer-aided semantic annotation of multimedia.
Starting from the acknowledgement of the weak points of fully automatic annotation, and the observed gap between manual and automated annotation approaches, this project sets the new goal
of combining human and machine intelligence to maximise the performance and benefits in a semi-manual annotation scheme. Therefore, instead of trying to substitute human intelligence,
the machine will compliment it.
Hence the novelty of CASAM lies in the difficult task of online aggregating human and machine knowledge with the ultimate target of minimising human involvement in the annotation procedure.
CASAM will move current research efforts towards new directions.
Knowledge representation and reasoning will play a central role, providing the semantics of the process. In this area, we will go beyond current research trends, into a
closer interaction with the both the multimedia analysis tools and the user, with the aim of optimising annotation performance and minimising the user's overhead.
On the side of multimedia analysis, we will follow a knowledge driven approach that will be able to focus on the context provided by both human and system knowledge.
Finally, in the interaction of the human and system, we will optimise the acquisition of the required information, through the knowledge inferred by the machine about a particular situation. .
Towards its main target, CASAM sets specific goals. These pertain to the increase of in annotation speed and accuracy compared to both manual and automated annotation.
Therefore, the usability of the derived measures and tools and the real-world performance will signal the success of the project.