A collaborative algorithm for semantic video annotation using a consensus-based social network analysis

Abstract

Social TV represents a new form of shopping that enables consumers to view, select and buy products. This highlights the need for a collaborative video annotation technique. This paper proposes a collaborative algorithm for semantic video annotation using consensus-based social network analysis (SNA). The collaborative video annotation process is organized based on social networks. Here, the media content is shared with friends of friends who collaboratively annotate it. This study used an ontology-based approach to semantically describe the media content and allow sharing between users. A consensus-based method was used to reconcile conflicts between participants’ annotations. The experimental results indicated that the use of SNA-based collaboration criteria to evaluate the collaborative process enhances the completeness and consistency of collaborative annotation. The more the collaboration criteria are satisfied by the collaborative group, the faster the group will reach a consensus. In addition, the consensus-based method is an effective approach for resolving conflicts on collaborative annotation.