| 摘要 |
A general framework for video search reranking is disclosed which explicitly formulates reranking into a global optimization problem from the Bayesian perspective. Under this framework, with two novel pair-wise ranking distances, two effective video search reranking methods, hinge reranking and preference strength reranking, are disclosed. Experiments conducted on the TRECVID dataset have demonstrated that the disclosed methods outperform several existing reranking approaches.
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