Fast and scalable keypoint recognition and image retrieval using binary codes

@article{Ventura2011FastAS,
  title={Fast and scalable keypoint recognition and image retrieval using binary codes},
  author={Jonathan Ventura and Tobias H{\"o}llerer},
  journal={2011 IEEE Workshop on Applications of Computer Vision (WACV)},
  year={2011},
  pages={697-702}
}
In this paper we report an evaluation of keypoint descriptor compression using as little as 16 bits to describe a single keypoint. We use spectral hashing to compress keypoint descriptors, and match them using the Hamming distance. By indexing the keypoints in a binary tree, we can quickly recognize keypoints with a very small database, and efficiently insert new keypoints. Our tests using image datasets with perspective distortion show the method to enable fast keypoint recognition and image… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 20 REFERENCES

Quantization schemes for low bitrate Compressed Histogram of Gradients descriptors

  • 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops
  • 2010
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Pose tracking from natural features on mobile phones

  • 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
  • 2008
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Spectral Hashing

VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Kernelized locality-sensitive hashing for scalable image search

  • 2009 IEEE 12th International Conference on Computer Vision
  • 2009
VIEW 2 EXCERPTS