BoostMap: A method for efficient approximate similarity rankings

@article{Athitsos2004BoostMapAM,
  title={BoostMap: A method for efficient approximate similarity rankings},
  author={Vassilis Athitsos and Jonathan Alon and Stan Sclaroff and George Kollios},
  journal={Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.},
  year={2004},
  volume={2},
  pages={II-II}
}
This paper introduces BoostMap, a method that can significantly reduce retrieval time in image and video database systems that employ computationally expensive distance measures, metric or non-metric. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. Embedding construction is formulated as a machine learning task, where AdaBoost is used to combine many simple, ID embeddings into a multidimensional… CONTINUE READING
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