/deep-supervised-hashing-DSH

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#This is the README information of the following publication

Deep Supervised Hashing for Fast Image Retrieval,

Version 1.0, Copyright(c) May, 2016

Haomiao Liu, Ruiping Wang, Shiguang Shan, Xilin Chen.

All Rights Reserved.

Example usage:

  1. Modify "Makefile.config" according to your system, and follow the instructions on "http://caffe.berkeleyvision.org/installation.html" to compile the source code.

  2. Run "data/cifar10/get_cifar10.sh" to download the data of CIFAR-10.

  3. Run "CIFAR-10/create_cifar10.sh" to convert the data of CIFAR-10 to LMDB format.

  4. Run "CIFAR-10/train_full.sh" to train an example model.

  5. Run "CIFAR-10/finetune.sh" to finetune an existing model for different code length.

  6. Run "CIFAR-10/extract_code.sh" to get the "real-valued" binary code of test images, stored in "code.dat", with each 12 values corresponding to a single image, and the labels of these images in "label.dat". You can modify this file to extract binary codes from other models.

  7. Run "CIFAR-10/test_map.m" to compute the retrieval mAP. It is normal if the result is higher than those reported in the original publication.

Tips:

  1. We have provided a tool for extracting network activations, you may refer to "CIFAR-10/extract_code.sh" for example usage. The example code extracts the real-valued network outputs, and writes them into a binary file. The first 12 floating point numbers correspond to the first image, and the second 12 correspond to the second image, and so on.

  2. We have also provided a "HashingImageData" layer which corresponds to the online image pair generation scheme described in our paper. This scheme is extremely useful for large scale dataset, where dissimilar pairs outnumber similar pairs. Please refer to "CIFAR-10/train_test.prototxt" for example usage.

  3. Our code was modified to support multi-label images as in NUS-WIDE, please refer to "NUS/train.txt", "NUS/test.txt", "NUS/create_NUS.sh" for example usages.

Please refer to the following paper if you find the source code helpful:

Haomiao Liu, Ruiping Wang, Shiguang Shan, Xilin Chen.

Deep Supervised Hashing for Fast Image Retrieval

In Proc. CVPR 2016.

Contact: haomiao.liu@vipl.ict.ac.cn

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