ABSTRACT

1
VACBIR
Visual Attentive Content Based Image Retrieval
A Content-Based Image Retrieval System Using
Models of Human Vision.
Adetokunbo Bamidele, EngD. Research
Engineer Department of Electrical and Electronics
Engineering 1st Supervisor Prof. Fred
Stentiford, 2nd Supervisor Dr. Lionel Sacks
Industrial Supervisor Jason Morphett a.bamidele_at_e
e.ucl.ac.uk or s.bamidele_at_cs.ucl.ac.uk
ABSTRACT
The world is in the midst of a content
explosion.  Widespread digital imaging technology
and the tumbling cost of processing and storage
devices has seen huge growth in the acquisition
and storage of content.  From amateur
photography to crime detection and medical
imaging, image based content, whether static or
dynamic is accounting for millions of terabytes
across the worlds databases.  Current image
based databases use predominantly traditional and
manual means to classify, archive, search and
retrieve data.
AIMS
Models of human visual attention and models of
the human recognition of visual similarities with
the aim of characterizing content in ways that
promote fast and effective search of large image
databases. 
Study database configurations that accommodate
new metadata attributes and their associated
functionality
At the services and applications layer and
possibly in other unexplored environments.
As a means to providing value added services for
present and future telecommunications generations
RESEARCH SCOPE

• Deriving new metadata models and integrating
  within an object-based data model.

• Producing Prototype VACBIR Applications for
  searching and retrieving content.

• Proposing new metadata standards to international
  bodies (e.g. MPEG-7/21)

• Extending existing models of human vision to
  handle wider ranges of content.

• Investigating how models of human
  vision can
• improve retrieval systems

• Investigating video applications.

www.ee.ucl.ac.uk/abamidel