IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Video search re-ranking via multi-graph propagation
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
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian video search reranking
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
Supervised reranking for web image search
Proceedings of the international conference on Multimedia
Learning from search engine and human supervision for web image search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Joint-rerank: a novel method for image search reranking
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
A bag-of-objects retrieval model for web image search
Proceedings of the 20th ACM international conference on Multimedia
A heterogenous automatic feedback semi-supervised method for image reranking
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The graph-based reranking methods have been proven effective in image and video search. The basic assumption behind them is the ranking score consistency, i.e., neighboring nodes (visually similar images or video shots) in a graph having close ranking scores, which is modeled through a regularizer term. The existing reranking methods utilise pair-wise regularizers, e.g., the Laplacian regularizer and the normalized Laplacian regularizer, to estimate the consistency over the graph from the pair-wise perspective by requiring the scores to be close for pairs of samples. However, since the consistency is a term defined over the whole set of neighboring samples, it is characterized by the local structure of the neighboring samples, i.e., the multiple-wise relations among the neighbors. The pair-wise regularizers fail to capture the desired property of consistency since they treat the neighboring samples independently. To tackle this problem, in this paper, we propose to use local learning regularizer to model the multiple-wise consistency, by formulating the consistent score estimation over a local area into a learning problem. Experiments on the TRECVID benchmark dataset and a real Web image dataset demonstrate the superiority of the local learning regularizer in visual reranking.