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.