@inproceedings{0af69d319fa3408ebd67d334226ebd0d,
title = "Forest hashing: Expediting large scale image retrieval",
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.",
keywords = "forest hashing, image retrieval, kd-tree, spectral hashing",
author = "Jonathan Springer and Xin Xin and Zhu Li and Jeremy Watt and Katsaggelos, {Aggelos K}",
year = "2013",
month = oct,
day = "18",
doi = "10.1109/ICASSP.2013.6637938",
language = "English (US)",
isbn = "9781479903566",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "1681--1684",
booktitle = "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings",
note = "2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference date: 26-05-2013 Through 31-05-2013",
}