Making large-scale support vector machine learning practical
Advances in kernel methods
Understanding and Using Context
Personal and Ubiquitous Computing
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An information-theoretic perspective of tf—idf measures
Information Processing and Management: an International Journal
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The Search: How Google and Its Rivals Rewrote the Rules of Business and Transformed Our Culture
The Search: How Google and Its Rivals Rewrote the Rules of Business and Transformed Our Culture
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Multimedia semantic indexing using model vectors
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A reranking approach for context-based concept fusion in video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Video search in concept subspace: a text-like paradigm
Proceedings of the 6th ACM international conference on Image and video retrieval
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
Semantic concept-based query expansion and re-ranking for multimedia retrieval
Proceedings of the 15th international conference on Multimedia
ContextSeer: context search and recommendation at query time for shared consumer photos
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Learned lexicon-driven interactive video retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Video retrieval using high level features: exploiting query matching and confidence-based weighting
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Collaborative video reindexing via matrix factorization
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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To exploit the co-occurrence patterns of semantic concepts while keeping the simplicity of context fusion, a novel reranking approach is proposed in this paper. The approach, called ordinal reranking, adjusts the ranking of an initial search (or detection) list based on the co-occurrence patterns obtained by using ranking functions such as ListNet. Ranking functions are by nature more effective than classification-based reranking methods in mining ordinal relationships. In addition, the ordinal reranking is free of the ad hoc thresholding for noisy binary labels and requires no extra offline learning or training data. To select informative concepts for reranking, we also propose a new concept selection measurement, wc-tf-idf, which considers the underlying ordinal information of ranking lists and is thus more effective than the feature selection algorithms for classification. Being largely unsupervised, the reranking approach to context fusion can be applied equally well to concept detection and video search. While being extremely efficient, ordinal reranking outperforms existing methods by up to 40% in mean average precision (MAP) for the baseline text-based search and 12% for the baseline concept detection over TRECVID 2005 video search and concept detection benchmark.