Modeling video hyperlinks with hypergraph for web video reranking - TAN, Hung-Khoon (Institutional Knowledge at Singapore Management University, 2008)
In this paper, we investigate a novel approach of exploiting visual-duplicates for web video reranking using hypergraph. Current graph-based reranking approaches consider mainly the pair-wise linking of keyframes and ignore reliability issues that are inherent in such representation. We exploit higher order relation to overcome the issues of missing links in visual-duplicate keyframes and in addition identify the latent relationships among keyframes. Based on hypergraph, we consider two groups of video threads: visual near-duplicate threads and story threads, to hyperlink web videos and describe the higher order information existing in video content. To facilitate reranking using random walk algorithm, the hypergraph is converted to a star-like graph using star expansion algorithm. Experiments on a dataset of 12,790 web videos show that hypergraph reranking can improve web video retrieval up to 45% over the initial ranked result by the video sharing websites and 8.3% over the pair-wise based graph reranking in mean average precision (MAP).