Event-Based Media Enrichment Using an Adaptive Probabilistic Hypergraph Model

Xueliang Liu, Meng Wang, Bao-Cai Yin, Benoit Huet, Xuelong Li
2015 IEEE Transactions on Cybernetics  
Nowadays, with the continual development of digital capture technologies and social media services, a vast number of media documents are captured and shared online to help attendees record their experience during events. In this paper, we present a method combining semantic inference and multimodal analysis for automatically finding media content to illustrate events using an adaptive probabilistic hypergraph model. In this model, media items are taken as vertices in the weighted hypergraph and
more » ... the task of enriching media to illustrate events is formulated as a ranking problem. In our method, each hyperedge is constructed using the K-nearest neighbors of a given media document. We also employ a probabilistic representation, which assigns each vertex to a hyperedge in a probabilistic way, to further exploit the correlation among media data. Furthermore, we optimize the hypergraph weights in a regularization framework, which is solved as a second-order cone problem. The approach is initiated by seed media and then used to rank the media documents using a transductive inference process. The results obtained from validating the approach on an event dataset collected from EventMedia demonstrate the effectiveness of the proposed approach.
doi:10.1109/tcyb.2014.2374755 pmid:26470061 fatcat:vz7hu5bihjgdtk72bkj2u4yoti