The basic classification techniques for organizing information are thesauri,
taxonomy and faceted classification. Topic map is relatively a new entrant to
this information space. Topic map standard describes how complex relationships
between abstract concepts and real world resources can be represented using XML
syntax.
Institutions all over the world are continuously exploring ways to use ICT in
improving teaching and learning effectiveness. The use of course web pages,
discussion groups, bulletin boards, and e-mails have shown considerable impact
on teaching and learning in significant ways, across all disciplines.
Recent advances in hardware sophistication related to graphics display, audio
and video devices made available a large number of multimedia and hypermedia
applications. These multimedia applications need to store and retrieve the
different forms of media like text, hypertext, graphics, still images,
animations, audio and video. Dance is one of the important cultural forms of a
nation and dance video is one such multimedia types. Archiving and retrieving
the required semantics from these dance media collections is a crucial and
demanding multimedia application.
Dance video is one of the important types of narrative videos with semantic
rich content. This paper proposes a new meta model, Dance Video Content Model
(DVCM) to represent the expressive semantics of the dance videos at multiple
granularity levels. The DVCM is designed based on the concepts such as video,
shot, segment, event and object, which are the components of MPEG-7 MDS. This
paper introduces a new relationship type called Temporal Semantic Relationship
to infer the semantic relationships between the dance video objects.
Dance videos are interesting and semantics-intensive. At the same time, they
are the complex type of videos compared to all other types such as sports, news
and movie videos. In fact, dance video is the one which is less explored by the
researchers across the globe. Dance videos exhibit rich semantics such as macro
features and micro features and can be classified into several types. Hence,
the conceptual modeling of the expressive semantics of the dance videos is very
crucial and complex.
Educational media mining is the process of converting raw media data from
educational systems to useful information that can be used to design learning
systems, answer research questions and allow personalized learning experiences.
Knowledge discovery encompasses a wide range of techniques ranging from
database queries to more recent developments in machine learning and language
technology. Educational media mining techniques are now being used in IT
Services research worldwide.