FYI: This project is a work in progress. If you'd like to run the training code, please make sure you already have the dataset (leveldb/lmdb) and ImageNet pre-train model!!!!!!

Implementation of the Supervised Semantics-preserving Deep Hashing (SSDH)

Created by Kevin Lin, Huei-Fang Yang, and Chu-Song Chen at Academia Sinica, Taipei, Taiwan.

We present a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. SSDH constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties. Compared to state-of-the-art results, SSDH achieves 26.30% (89.68% vs. 63.38%), 17.11% (89.00% vs. 71.89%) and 19.56% (31.28% vs. 11.72%) higher precisions averaged over a different number of top returned images for the CIFAR-10, NUS-WIDE, and SUN397 datasets, respectively.

This modified caffe distribution provides the proposed objective function to learn efficient binary hash codes.

The details can be found in the following arXiv preprint.

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

Supervised Learning of Semantics-Preserving Hashing via Deep Neural Networks for Large-Scale Image Search
Huei-Fang Yang, Kevin Lin, Chu-Song Chen
arXiv preprint arXiv:1507.00101