Query expansion using probabilistic local feedback with application to multimedia retrieval

Abstract

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.