This repository is the implementation of Scalable Discrete Supervised Hash Learning with Asymmetric Matrix Factorization (Full paper version, the short paper version is to appear on ICDM'16).

• numpy
• scipy
• caffe

1. Download the cifar-10 data(origin image in RGB format and GIST descriptor) in npy format and extract them to the root folder (link)

• Run python dish-d.py (for deep hashing) or python dish-k.py (for kernel-based hashing).
• NOTE: you may run python resize_256.py and download the weights of VGG-16 net before using VGG net for training.