Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval

@inproceedings{Kim2003QclusterRF,
  title={Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval},
  author={Deok-Hwan Kim and Chin-Wan Chung},
  booktitle={SIGMOD Conference},
  year={2003}
}
The learning-enhanced relevance feedback has been one of the most active research areas in content-based image retrieval in recent years. However, few methods using the relevance feedback are currently available to process relatively complex queries on large image databases. In the case of complex image queries, the feature space and the distance function of the user's perception are usually different from those of the system. This difference leads to the representation of a query with multiple… CONTINUE READING

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Key Quantitative Results

  • Extensive experiments show that the result of our method converges to the user's true information need fast, and the retrieval quality of our method is about 22% in recall and 20% in precision better than that of the query expansion approach, and about 34% in recall and about 33% in precision better than that of the query point movement approach, in MARS.

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