Leveraging collective wisdom for web video retrieval through heterogeneous community discovery

With the exponential growth of social media, web video retrieval based on contextual information associated with videos has attracted wide attention recently. However, state-of-the-art methods mainly focus on limited kinds of context cues and lack of unified exploration towards multiple heterogeneous contexts. In this paper, we propose a novel web video ranking framework called CommunityRank by leveraging the collective wisdom from a community perspective. Firstly, it formulizes various social relations among users, videos and tags in a heterogeneous context network and further detects its latent community structure. Then the algorithm maps videos into the community space and performs a community-oriented re-ranking through a bipartite graph model. By aggregating the multiple relations, CommunityRank can make the most of textual, visual and social contexts and leads to better search results. The encouraging performances of the proposed method on YouTube video collection demonstrate that the discovered communities reveal topics of interest emerging in collective behaviors and can facilitate web video retrieval.