Video search reranking via online ordinal reranking

To exploit co-occurrence patterns among features and target semantics while keeping the simplicity of the keywordbased visual search, a novel reranking methods is proposed. The approach, ordinal reranking, reranks an initial search list by utilizing the co-occurrence patterns via the ranking functions such as ListNet. Ranking functions are by nature more effective than classification-based reranking methods in mining ordinal relationships. In addition, ordinal reranking is ease of the ad-hoc thresholding for noisy binary labels and requires no extra off-line learning or training data. When evaluated in TRECVID search benchmark, ordinal reranking, while being extremely efficient, outperforms existing methods and offers 35.6% relative improvement over the text-based search baseline in nearly real time.

• Type: Conference
• Year: 2008
• Where: ICMCS
• Authors: Yi-Hsuan Yang, Winston H. Hsu
• Keywords: Co-occurrence Patterns, ICMCS 2008, Multimedia, Ordinal Reranking, Reranking Methods