A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Negative pseudo-relevance feedback in content-based video retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Flexible pseudo-relevance feedback via selective sampling
ACM Transactions on Asian Language Information Processing (TALIP)
Regularized estimation of mixture models for robust pseudo-relevance feedback
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Applying Data Mining to Pseudo-Relevance Feedback for High Performance Text Retrieval
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A nearest-neighbor approach to relevance feedback in content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
A cluster-based resampling method for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
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Pseudo Relevance Feedback (PRF) has shown effective performance in information retrieval, but it has seldom been applied in the area of high level feature detection (HLF). In this paper, we explicitly propose to introduce PRF into HLF. Our contributions mainly lie in two-fold: (1) proposing three novel PRF approaches to extract pseudo positive samples, i.e., Nearest-Neighbor (NN) based PRF, Score-Evaluation (SE) based PRF and Multi-Classifier Decision (MCD) based PRF; (2) utilizing incremental learning to reduce the re-training time. We evaluate our approaches on the benchmark of TRECVID2008. Reported results have shown that MCD based approach outperforms the other two and obtain an excellent gain in average precision with respect to the baseline without PRF.