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Neighbourhood Preserving Quantisation (NPQ) code
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NPQ
SBQ
baselines
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README.md
initialise.m
run_quant.m

README.md

Neighbourhood Preserving Quantisation for LSH

Current version: 0.1. Distributed under a Creative Commons Attribution-NonCommercial License: http://creativecommons.org/licenses/by-nc/4.0/deed.en_US

This code is an implementation of the Neighbourhood Preserving Quantisation (NPQ) model introduced in the paper:

Sean Moran, Victor Lavrenko and Miles Osborne. Neighbourhood Preserving Quantisation for LSH. SIGIR, 2013

Prerequisites:

  1. Matlab

If you use the NPQ code for a publication, please cite the following paper:

@inproceedings{Moran:2013:NPQ:2484028.2484162, author = {Moran, Sean and Lavrenko, Victor and Osborne, Miles}, title = {Neighbourhood Preserving Quantisation for LSH}, booktitle = {Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval}, series = {SIGIR '13}, year = {2013}, isbn = {978-1-4503-2034-4}, location = {Dublin, Ireland}, pages = {1009--1012}, numpages = {4}, url = {http://doi.acm.org/10.1145/2484028.2484162}, doi = {10.1145/2484028.2484162}, acmid = {2484162}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {approximate nearest neighbour search, hamming distance, image retrieval, locality sensitive hashing, manhattan distance}, }

Usage

  1. Obtain the pre-processed dataset files for MNIST, CIFAR-10 and NUSWIDE here: https://www.dropbox.com/sh/pvso066sqd2z8ja/AABu7dxMx92lhlLLXLUpg_jMa?dl=0

  2. Edit the properties in initialise.m to fit your system and requirements (e.g. hashcode length, dataset, amount of supervision, paths to datasets and results directory etc).

  3. run_quant.m

Copyright

Copyright (C) by Sean Moran, University of Edinburgh

Please send any bug reports to sean.j.moran@gmail.com

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