Speeding up active relevance feedback with approximate kNN retrieval for hyperplane queries

@inproceedings{Crucianu2008SpeedingUA,
  title={Speeding up active relevance feedback with approximate kNN retrieval for hyperplane queries},
  author={Michel Crucianu and Daniel Estevez and Vincent Oria and Jean-Philippe Tarel},
  year={2008}
}
In content-based image retrieval, relevance feedback (RF) is a prominent method for reducing the “semantic gap” between the low-level features describing the content and the usually higher-level meaning of user's target. Recent RF methods are able to identify complex target classes after relatively few feedback iterations. However, because the computational complexity of such methods is linear in the size of the database, retrieval can be quite slow on very large databases. To address this… CONTINUE READING