Similarity-Adaptive Deep Hashing (SADH)

Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization

Created by Fumin Shen, Yan Xu, Li Liu, Yang Yang, Zi Huang, Heng Tao Shen

The details can be found in the TPAMI 2018 paper.

Contents

• Prerequisites
• Installation
• Usage
• Resources
• Citation

Prerequisites

1. Requirements for Caffe, pycaffe and matcaffe (see: Caffe installation instructions).

2. Prerequisites for datasets.

   Note: In our experiments, we horizontally flip training images manually for data augmentation. If the size of your training data is small (< 100K, like CIFAR-10. MNIST), you should do this step.

   We also provide our flipping code in cifar10/flip_img.m, you can run it to handle your own datasets.

3. VGG-16 pre-trained model on ILSVC12 datasets, and save it in caffemodels directory.

Installation

Enter caffe directory and download the source codes.

    cd caffe/

Modify Makefile.config and build Caffe with following commands:

    make all -j8
    make pycaffe
    make matcaffe

Usage

We only supply the code to train 16-bit SADH on CIFAR-10 dataset.

We integrate train step and test step in a bash file train.sh, please run it as follows:

    ./train.sh [ROOT_FOLDER] [GPU_ID]
    # ROOT_FOLDER is the root folder of image datasets, e.g. ./cifar10/
    # GPU_ID is the GPU you want to train on

Resources

We supply CIFAR-10 dataset, which has been flipped. You can download it by following links:

• CIFAR-10 dataset (png format): BaiduYun (Updated).

Citation

If you find our approach useful in your research, please consider citing:

@article{'shen2018tpami',
    author   = {Fumin Shen and Yan Xu and Li Liu and Yang Yang and Zi Huang and Heng Tao Shen},
    journal  = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, 
    title    = {Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization},
    year     = {2018}
}