Interactive pattern analysis for relevance feedback in multimedia information retrieval

@article{Wu2004InteractivePA,
  title={Interactive pattern analysis for relevance feedback in multimedia information retrieval},
  author={Yimin Wu and Aidong Zhang},
  journal={Multimedia Systems},
  year={2004},
  volume={10},
  pages={41-55}
}
Abstract.Relevance feedback is a mechanism to interactively learn a user’s query concept online. It has been extensively used to improve the performance of multimedia information retrieval. In this paper, we present a novel interactive pattern analysis method that reduces relevance feedback to a two-class classification problem and classifies multimedia objects as relevant or irrelevant. To perform interactive pattern analysis, we propose two online pattern classification methods, called… CONTINUE READING

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Key Quantitative Results

  • Extensive experiments on a large-scale image database (with 31,438 COREL images) demonstrate that our method outperforms the state-of-the-art approaches [15,28] by at least 20% on average precision and recall.

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