Tag-Based Social Image Search with Hyperedges Correlation

In social image search, most existing hypergraph methods use the visual and textual features in isolation by treating each feature term as a hyperedge. Nevertheless, they neglect the correlations of visual and textual hyperedges, which are more robust to represent the high-order relationship among vertices. In this paper, we propose a hypergraph with correlated hyperedges (CHH), which introduces high-order relationship of hyperedges into hypergraph learning. Based on CHH, a pairwise visual-textual correlation hypergraph (VTCH) model is used for tagbased social image search. To overcome the large number of newly generated hybrid hyperedges, a bagging-based method is adopted to balance the accuracy and speed. Finally, adaptive hyperedges learning method is used to obtain the relevance score for social image search. The experiments conducted on MIR Flickr show the effectiveness of our proposed method.