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A Unified Optimization Based Learning Method for Image Retrieval
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
Graph based multi-modality learning
Proceedings of the 13th annual ACM international conference on Multimedia
Manifold-ranking based video concept detection on large database and feature pool
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 6th ACM international conference on Image and video retrieval
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Proceedings of the 6th ACM international conference on Image and video retrieval
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Proceedings of the 6th ACM international conference on Image and video retrieval
Structure-sensitive manifold ranking for video concept detection
Proceedings of the 15th international conference on Multimedia
Optimizing multi-graph learning: towards a unified video annotation scheme
Proceedings of the 15th international conference on Multimedia
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
Optimizing video search reranking via minimum incremental information loss
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Measuring semantic similarity by latent relational analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Multimedia search with pseudo-relevance feedback
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LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Interactive video search using multilevel indexing
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Visual search reranking via adaptive particle swarm optimization
Pattern Recognition
Correlation-Based Ranking for Large-Scale Video Concept Retrieval
International Journal of Multimedia Data Engineering & Management
Memory recall based video search: Finding videos you have seen before based on your memory
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Most of the existing learning-based methods for video search take query examples as "positive" and build a model for each query. These methods, referred to as query-dependent, only achieve limited success as users are mostly reluctant to provide enough query examples. To address this problem, we propose a novel query-independent learning approach based on multigraph to video search, which learns the relevance information existing in the query-shot pairs. The proposed approach, named MG-QIL, is more general and suitable for a real-world video search system as the learned relevance is independent of any queries. Specifically, MG-QIL constructs multiple graphs, including a main-graph covering all the pairs and a set of subgraphs covering the pairs within the same query. The pairs in the main-graph are connected in terms of relational similarity, while the pairs in the subgraphs for the same query are connected in terms of attributional similarity. The relevance labels are then propagated in the multiple graphs until convergence. We conducted extensive experiments on automatic search tasks over the TRECVID 2005-2007 benchmark and the results show a superior performance to state-of-the-art approaches to video search. Furthermore, when applied to video search reranking, MG-QIL can also achieve significant and consistent improvement over a text search baseline.