@inproceedings{Jonathan2013,
abstract = {This paper introduces a hybrid method for searching large image datasets for approximate nearest neighbor items, specifically SIFT descriptors. The basic idea behind our method is to create a serial system that first partitions approximate nearest neighbors using multiple kd-trees before calling upon locally designed spectral hashing tables for retrieval. This combination gives us the local approximate nearest neighbor accuracy of kd-trees with the computational efficiency of hashing techniques. Experimental results show that our approach efficiently and accurately outperforms previous methods designed to achieve similar goals. {\textcopyright} 2013 IEEE.},
author = {Springer, Jonathan and Xin, Xin and Li, Zhu and Watt, Jeremy and Katsaggelos, Aggelos},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
doi = {10.1109/ICASSP.2013.6637938},
isbn = {978-1-4799-0356-6},
issn = {15206149},
keywords = {forest hashing,image retrieval,kd-tree,spectral hashing},
month = {may},
pages = {1681--1684},
publisher = {IEEE},
title = {{Forest hashing: Expediting large scale image retrieval}},
url = {http://ieeexplore.ieee.org/document/6637938/},
year = {2013}
}

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