Collaborating Educational Videos with Presenter Profiles for Effective Content- based Video Retrieval

E-learning has become an integral part in higher education in the last decade. The emerging multimedia information technologies will 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 focus of this paper is on video based educational material where presenters deliver educational content. We have developed a system which is capable of storing educational video clips with their semantics and retrieving required video clip segments efficiently on their semantics. The system creates profiles of presenters appearing in the video clips based on their facial features and uses these profiles to partition similar video clips into logical meaningful segments. We address one of the main problems identified in profile construction and propose a novel approach to create the profiles by introducing a profile normalization algorithm. In particular, this method places more effort on solving the profile overlapping problem by using certain parameters. The effectiveness of the normalizing algorithm was tested by comparing Total Error Rate (TER) when the normalizing process is avoided versus together with the normalizing method. The results show that the insertion of profile normalizing method reduces TER by 38% to 19%. Improving these techniques for lecture videos has significant educational and social benefits.