################ Information ################
Matlab demo code for "FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval" accepted by IEEE Transactions on Neural Networks and Learning Systems Authors: Xin Liu, Xingzhi Wang, and Yiu-ming Cheung; Contact: xliu[at]hqu.edu.cn
This code uses some public software packages by the 3rd party applications, and is free for educational, academic research and non-profit purposes. Not for commercial/industrial activities. If you use/adapt our code in your work (either as a stand-alone tool or as a component of any algorithm), you need to appropriately cite our work.
################ Tips ################
- To run a demo, see the FDDH_main package and conduct the following command: trainFDDH.m
- If you have got any question, please do not hesitate to contact us.
- Bugs are also welcome to be reported.
################ Contents ################ This package contains cleaned up codes for the FDDH, including:
trainFDDH.m: test example on public Wiki dataset solveFDDH.m: function to optimize the objective function of FDDH bitCompact.m: function to compute the compact hash code matrix hammingDist.m: function to compute the hamming distance between two sets kernelMatrix.m: function to compute a kernel matrix kernelTrans: function to do kernel transformation centerlizeData: function to centerlize the data calcPreRecRadiusLabel.m: calculate precision and recall within different radius based on Label calcMapTopkMapTopkPreTopkRecLabel.m: function to obtain the retrieval results.
If you use our codes, we are appreciated if you appropriately cite our work. ################ Citation ################ Xin Liu, Xingzhi Wang and Yiu-ming Cheung; "FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval", IEEE Transactions on Neural Networks and Learning Systems, doi:10.1109/TNNLS.2021.3076684