Recently, learning based hashing techniques have attracted broad research interests because they can support efficient storage and retrieval for high-dimensional data such as images, videos, documents, etc. However, a major difficulty of learning to hash lies in handling the discrete constraints imposed on the pursued hash codes, which typically makes hash optimizations very challenging (NPhard in general). The widely adopted relaxation scheme simplifies the optimization, however, tends to produce less effective codes.
In this project, we investigate how to directly learn binary codes via discrete optimization, namely discrete hashing. Discrete hashing has been shown to significantly boost the performance of hashing algorithms (e.g., Supervised Discrete Hashing) in large-scale similarity search.
@inproceedings{'liu2017coding',
author = {Li Liu and Mengyang Yu and Fumin Shen and Ling Shao},
booktitle = {Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title = {Discretely Coding Semantic Rank Orders for Image Hashing},
year = {2017}
}
@ARTICLE{TIP2016binary,
author={F. Shen and X. Zhou and Y. Yang and J. Song and H. T. Shen and D. Tao},
journal={IEEE Transactions on Image Processing},
title={A Fast Optimization Method for General Binary Code Learning},
year={2016},
volume={25},
number={12},
pages={5610-5621},
}
@InProceedings{shen2015cvpr,
author = {Shen, Fumin and Shen, Chunhua and Liu, Wei and Tao Shen, Heng},
title = {Supervised Discrete Hashing},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {37-45},
month = {June},
year = {2015}
}
@InProceedings{Shen_2015_ICCV,
author = {Shen, Fumin and Liu, Wei and Zhang, Shaoting and Yang, Yang and Tao Shen, Heng},
title = {Learning Binary Codes for Maximum Inner Product Search},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
pages = {4148--4156},
year = {2015}
}