/GCH

Graph Convolutional Network Hashing for Cross-Modal Retrieval, IJCAI2019

Primary LanguagePython

GCH

Graph Convolutional Network Hashing for Cross-Modal Retrieval, IJCAI2019

Introduction

we propose a Graph Convolutional Hashing (GCH) approach, which learns modality-unified binary codes via an affinity graph. For more details, please refer to our paper.

Citation

@inproceedings{xu2019graph,
title={Graph Convolutional Network Hashing for Cross-Modal Retrieval.},
author={Xu, Ruiqing and Li, Chao and Yan, Junchi and Deng, Cheng and Liu, Xianglong},
booktitle={Ijcai},
pages={982--988},
year={2019}
}

Prerequisites

  • Python 2.7
  • Tensorflow 1.2.0
  • Others (numpy, scipy, h5py, etc.)

Installation

  1. Clone the repository

    git clone https://github.com/DeXie0808/GCH.git

  2. Prepare the dataset and the pretrained model.

  • Dataset: Flickr25k dataset Please download Flickr25k dataset: FLICKR-25k.mat and place the data under ./data.

  • Pretrained model: vggf Please download the pretrained vggf model: imagenet-vgg-f.mat and place the data under ./data/weight.

  • Mean of ImageNet: mean Please download the mean of the ImageNet: Mean.h5 and place the data under ./data/weight.

Training

  1. Train the model, run the code under main folder. Change setting.py, use phase='train'

    python main_itpair.py

  2. Evaluate the model, run the code under main folder. Change setting.py, use phase='test'

    python main_itpair.py