Video search reranking via online ordinal reranking
Resource
Multimedia and Expo, 2008 IEEE International Conference on, 285-288
Journal
2008 IEEE International Conference on Multimedia and Expo
Pages
285-288
Date Issued
2008
Date
2008
Author(s)
Abstract
To exploit co-occurrence patterns among features and target semantics while keeping the simplicity of the keyword-based 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.
SDGs
Type
conference paper
File(s)![Thumbnail Image]()
Loading...
Name
05.pdf
Size
23.23 KB
Format
Adobe PDF
Checksum
(MD5):4f5e229c61be7380ea5015ec19c46fd3