This paper tackles the problem of video concept detection using the multi-modality fusion method. Motivated by
multi-view learning algorithms, multi-modality features of videos can be represented by multiple graphs. And the graph-based
semi-supervised learning methods can be extended to multiple graphs to predict the semantic labels for unlabeled video data.
However,  traditional  graphs  represent  only  homogeneous  pairwise  linking  relations,  and  therefore  the  high-order  correlations
inherent in videos, such as high-order visual similarities, are ignored. In this paper we represent heterogeneous features by multiple
hypergraphs and then the high-order correlated samples can be associated with hyperedges. Furthermore, the multi-hypergraph
ranking (MHR) algorithm is proposed by defining Markov random walk on each hypergraph and then forming the mixture Markov
chains  so  as  to  perform  transductive  learning  in  multiple  hypergraphs.  In  experiments  on  the  TRECVID  dataset,  a  triple-
hypergraph consisting of visual, textual features and multiple labeled tags is constructed to predict concept labels for unlabeled
video shots by the MHR framework. Experimental results show that our approach is effective.

关键词： Multiple hypergraph ranking,  Video concept detection,  Multi-view learning,  Multiple labeled tags,  Clustering