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A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval

  • Johannes L. SchönbergerEmail author
  • True Price
  • Torsten Sattler
  • Jan-Michael Frahm
  • Marc Pollefeys
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10111)

Abstract

Spatial verification is a crucial part of every image retrieval system, as it accounts for the fact that geometric feature configurations are typically ignored by the Bag-of-Words representation. Since spatial verification quickly becomes the bottleneck of the retrieval process, runtime efficiency is extremely important. At the same time, spatial verification should be able to reliably distinguish between related and unrelated images. While methods based on RANSAC’s hypothesize-and-verify framework achieve high accuracy, they are not particularly efficient. Conversely, verification approaches based on Hough voting are extremely efficient but not as accurate. In this paper, we develop a novel spatial verification approach that uses an efficient voting scheme to identify promising transformation hypotheses that are subsequently verified and refined. Through comprehensive experiments, we show that our method is able to achieve a verification accuracy similar to state-of-the-art hypothesize-and-verify approaches while providing faster runtimes than state-of-the-art voting-based methods.

Keywords

Image Retrieval Visual Word Query Image Query Expansion Vote Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

True Price and Jan-Michael Frahm were supported in part by the NSF No. IIS-1349074, No. CNS-1405847.

Supplementary material

416257_1_En_21_MOESM1_ESM.pdf (176 kb)
Supplementary material 1 (pdf 175 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Johannes L. Schönberger
    • 1
    Email author
  • True Price
    • 2
  • Torsten Sattler
    • 1
  • Jan-Michael Frahm
    • 2
  • Marc Pollefeys
    • 1
    • 3
  1. 1.ETH ZürichZürichSwitzerland
  2. 2.UNC Chapel HillChapel HillUSA
  3. 3.MicrosoftRedmondUSA

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