Abstract:
E-learning has become an integral part in higher education in the last decade. The
emerging multimedia information technologies allow researchers to identify new ways to
store, retrieve, share, and manipulate complex information which are expected to be used
for building exciting new e-learning applications. The key challenges in this field are
related to data organization and integration, indexing and retrieval mechanisms,
intelligent searching techniques, information browsing, content-based query processing,
handling of heterogeneity etc.
This thesis reveals a profile based feature identification system for multimedia database
systems which is designed to support the use of video clips for e-learning. The system
creates profiles of presenters appearing in the video clips based on their facial features
and uses these profiles to identify similar video segments based on the presenter profiles.
The face recognition algorithm used by the system is based on the Principal Components
Analysis (PCA) approach. The thesis addresses one of the main problems identified in
profile construction over video key-frames which is the overlapping of key-frames in the
eigenspace. It explains various tests carried out to explore the courses for this problem
and then proposes a novel approach to overcome the problem by introducing a profile
normalization algorithm. In particular, this method reveals the profile overlapping
problem can be controlled by using certain parameters obtained by analyzing a collection
of key-frames.
