Distributed Kd-trees for retrieval from very large image collections

Mohamed Aly, Mario Munich, Pietro Perona

Research output: Contribution to conferencePaperpeer-review

39 Scopus citations

Abstract

Distributed Kd-Trees is a method for building image retrieval systems that can handle hundreds of millions of images. It is based on dividing the Kd-Tree into a "root subtree" that resides on a root machine, and several "leaf subtrees", each residing on a leaf machine. The root machine handles incoming queries and farms out feature matching to an appropriate small subset of the leaf machines. Our implementation employs the MapRe-duce architecture to efficiently build and distribute the Kd-Tree for millions of images. It can run on thousands of machines, and provides orders of magnitude more throughput than the state-of-the-art, with better recognition performance. We show experiments with up to 100 million images running on 2048 machines, with run time of a fraction of a second for each query image.

Original languageEnglish (US)
DOIs
StatePublished - Jan 1 2011
Event2011 22nd British Machine Vision Conference, BMVC 2011 - Dundee, United Kingdom
Duration: Aug 29 2011Sep 2 2011

Other

Other2011 22nd British Machine Vision Conference, BMVC 2011
CountryUnited Kingdom
CityDundee
Period08/29/1109/2/11

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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