/gqr

Primary LanguageC++GNU General Public License v3.0GPL-3.0

GQR

A General and Efficient Querying Method for Learning to Hash (SIGMOD 2018)

Why GQR framework for similarity search

  • Fast Training (Matlab and Automatic Parallelization)
  • Support of Multiple Hash Tables (Like k-nn Graph supports k > 1)

Dependences

  • CMake
  • Matlab

Run

-Step 1: clone the repository

git clone --recursive https://github.com/lijinf2/gqr.git

cd gqr

-Step 2: train models, which are stored under folder hashingCodeTXT

cd learn/PCAH

mkdir hashingCodeTXT && matlab < pcah.m

cd ../../

-Step 3: make app and run

mkdir build && cd ./build && cmake ../ -DCMAKE_BUILD_TYPE=Release

make search

cd ../script && bash search.sh

You may refer to folder ./script for detailed explanations and more instructions.

Support

Hashing Algorithms

  • ITQ: Iterative quantization
  • PCAH: PCA Hashing
  • PCARR: PCA hashing with random rotation
  • SH: Spectral Hashing
  • SpH: Spherical Hashing
  • KMH: KMeans Hashing
  • IsoH: Isotropic Hashing
  • SIM: Random Projecting Hashing.
  • LMIP: Length Marked (variant length) Inner Product
    • only work with query method LM
    • based on SIM which generate random projecting bits, and extra bits is generated for representing NORM(Length)

Query Algorithms

  • HR: Hamming Ranking
  • HL: Hash Lookup
  • GQR: Generate-to-probe quantization ranking
  • LM: Length Marked ranking(work the LMIP)
    • use both random projecting bits and extra bis generated in LMIP to rank

Acknowledgement

GQR project is developed based on LSHBOX (https://github.com/RSIA-LIESMARS-WHU/LSHBOX) and MatlabFunc (https://github.com/dengcai78/MatlabFunc). Great appreciation to the contributors of LSHBOX and MatlabFunc.

More

Distribued GQR on general system for similarity search

Reference

A General and Efficient Querying Method for Learning to Hash (SIGMOD 2018)