Hashing with binary autoencoders

@article{CarreiraPerpin2015HashingWB,
  title={Hashing with binary autoencoders},
  author={Miguel {\'A}. Carreira-Perpi{\~n}{\'a}n and Ramin Raziperchikolaei},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  pages={557-566}
}
An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approaches approximate the optimization by relaxing the constraints and then binarizing the result. Here, we focus on the binary autoencoder model, which seeks to reconstruct an image… CONTINUE READING
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References

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Showing 1-10 of 28 references

Itera - tive quantization : A Procrustean approach to learning binary codes for large - scale image retrieval

S. Lazebnik Y. Gong, A. Gordo, F. Perronnin
IEEE Trans . Pattern Analysis and Machine Intelligence • 2013

Binarize the codes Z = g(X) by an optimal rotation: E(B,R) = ‖B−RZ‖2F

ITQ, Gong
N. p • 2012

Kernelized localitysensitive hashing

K. Grauman
IEEE Trans . Pattern Analysis and Machine Intelligence • 2012

Product Quantization for Nearest Neighbor Search

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2011

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