Using probabilistic models of document retrieval without relevance information
Document retrieval systems
Probabilistic models in information retrieval
The Computer Journal - Special issue on information retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Learning query-class dependent weights in automatic video retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
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
Probabilistic latent query analysis for combining multiple retrieval sources
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
TRECVID: benchmarking the effectiveness of information retrieval tasks on digital video
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
A few examples go a long way: constructing query models from elaborate query formulations
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 18th international conference on World wide web
A generative blog post retrieval model that uses query expansion based on external collections
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A language model approach for tag recommendation
Expert Systems with Applications: An International Journal
Exploiting External Collections for Query Expansion
ACM Transactions on the Web (TWEB)
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
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As one of the most effective query expansion approaches, local feedback is able to automatically discover new query terms and improve retrieval accuracy for different retrieval models. However, the performance of local feedback is heavily dependent on the assumption that most top-ranked documents are relevant to the query topic. Although this assumption might be sensible for ad-hoc text retrieval, it is usually violated in many other retrieval tasks such as multimedia retrieval. In this paper, we develop a robust local analysis approach called probabilistic local feedback (PLF) based on a discriminative probabilistic retrieval framework. The proposed model is effective for improving retrieval accuracy without assuming the most top-ranked documents are relevant. It also provides a sound probabilistic interpretation and a convergence guarantee on the iterative result updating process. Although derived from variational techniques, this approach only involves an iterative process of simple operations on ranking features and thus can be computed efficiently in practice. Our multimedia retrieval experiments on TRECVID'03-'05 collections have demonstrated the advantage of the proposed PLF approaches which can achieve noticeable gains in terms of mean average precision over various baseline methods and PRF-augmented results.