Semantic image annotation with social context

The purpose of our system is to semi-automatically generate ontology-based annotations for a social network by leveraging the annotations provided by the most active user (also known as the central actor). Social context of an image is of central importance in our approach towards semantic semi-automatic annotation. For the context of an image, we consider several factors like geo-reference, time and relationship among actors in social networks. Our system leverages the context, which is automatically available along with the image. We have also extended Social Network Analysis techniques by considering the granularity of relationships among actors under consideration. In this paper a semantic web framework was used to represent the social network and to deal with the diversity of relationships. OntoCAIM ontology is developed which not only encompasses Social Network Analysis functionality but also defines mechanism to annotate the images with an underlying ontology.