CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Higher order learning with graphs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
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 re-ranking via multi-graph propagation
Proceedings of the 15th international conference on Multimedia
Practical elimination of near-duplicates from web video search
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
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning
IEEE Transactions on Multimedia
Multilevel spectral hypergraph partitioning with arbitrary vertex sizes
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Mining near-duplicate graph for cluster-based reranking of web video search results
ACM Transactions on Information Systems (TOIS)
Towards more precise social image-tag alignment
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Leveraging exemplar and saliency model for image search reranking
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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In this paper, we investigate a novel approach of exploiting visual-duplicates for web video reranking using hypergraph. Current graph-based reranking approaches consider mainly the pair-wise linking of keyframes and ignore reliability issues that are inherent in such representation. We exploit higher order relation to overcome the issues of missing links in visual-duplicate keyframes and in addition identify the latent relationships among keyframes. Based on hypergraph, we consider two groups of video threads: visual near-duplicate threads and story threads, to hyperlink web videos and describe the higher order information existing in video content. To facilitate reranking using random walk algorithm, the hypergraph is converted to a star-like graph using star expansion algorithm. Experiments on a dataset of 12,790 web videos show that hypergraph reranking can improve web video retrieval up to 45% over the initial ranked result by the video sharing websites and 8.3% over the pair-wise based graph reranking in mean average precision (MAP).