Efficient image matching with distributions of local invariant features

@article{Grauman2005EfficientIM,
  title={Efficient image matching with distributions of local invariant features},
  author={Kristen Grauman and Trevor Darrell},
  journal={2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)},
  year={2005},
  volume={2},
  pages={627-634 vol. 2}
}
Sets of local features that are invariant to common image transformations are an effective representation to use when comparing images; current methods typically judge feature sets' similarity via a voting scheme (which ignores co-occurrence statistics) or by comparing histograms over a set of prototypes (which must be found by clustering). We present a method for efficiently comparing images based on their discrete distributions (bags) of distinctive local invariant features, without… CONTINUE READING
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