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CIKM
2007
Springer

Query expansion using probabilistic local feedback with application to multimedia retrieval

14 years 11 days ago
Query expansion using probabilistic local feedback with application to multimedia retrieval
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 sim...
Rong Yan, Alexander G. Hauptmann
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where CIKM
Authors Rong Yan, Alexander G. Hauptmann
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