Modeling personal and social network context for event annotation in images
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Real-time near-duplicate elimination for web video search with content and context
IEEE Transactions on Multimedia - Special issue on integration of context and content
Learning social tag relevance by neighbor voting
IEEE Transactions on Multimedia
SNDocRank: a social network-based video search ranking framework
Proceedings of the international conference on Multimedia information retrieval
Co-reranking by mutual reinforcement for image search
Proceedings of the ACM International Conference on Image and Video Retrieval
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Proceedings of the 20th ACM international conference on Multimedia
Collective search and recommendation in social media
Proceedings of the 20th ACM international conference on Multimedia
Social influence analysis and application on multimedia sharing websites
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
Hi-index | 0.00 |
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.