SDSH Code

Summary

Python implemetation can be accessed from this repository.

Related publications include (Pidhorskyi et al., 2018).

References

ACCV
Deep supervised hashing with spherical embedding Pidhorskyi, S., Jones, Q., Motiian, S., Adjeroh, D., and Doretto, G. In Proceedings of The Asian Conference on Computer Vision, 2018. abstract bibTeX pdf
Deep hashing approaches are widely applied to approximate nearest neighbor search for large-scale image retrieval. We propose Spherical Deep Supervised Hashing (SDSH), a new supervised deep hashing approach to learn compact binary codes. The goal of SDSH is to go beyond learning similarity preserving codes, by encouraging them to also be balanced and to maximize the mean average precision. This is enabled by advocating the use of a different relaxation method, allowing the learning of a spherical embedding, which overcomes the challenge of maintaining the learning problem well-posed without the need to add extra binarizing priors. This allows the formulation of a general triplet loss framework, with the introduction of the spring loss for learning balanced codes, and of the ability to learn an embedding quantization that maximizes the mean average precision. Extensive experiments demonstrate that the approach compares favorably with the state-of-the-art while providing significant performance increase at more compact code sizes.
```
@inproceedings{pidhorskyiJMAD18accv,
  abbr = {ACCV},
  author = {Pidhorskyi, S. and Jones, Q. and Motiian, S. and Adjeroh, D. and Doretto, G.},
  title = {Deep supervised hashing with spherical embedding},
  booktitle = {Proceedings of The Asian Conference on Computer Vision},
  year = {2018},
  pages = {417--434},
  bib2html_pubtype = {Conferences}
}
```