Forest hashing: Expediting large scale image retrieval

Jonathan Springer, Xin Xin, Zhu Li, Jeremy Watt, Aggelos K Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages1681-1684
Number of pages4
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • forest hashing
  • image retrieval
  • kd-tree
  • spectral hashing

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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