Multiple unordered wide-baseline image matching and grouping

Zhoucan He, Qing Wang, Heng Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper focuses on the multi-view feature matching problem from unordered image sets. Firstly, an efficient and effective high dimensional feature matching algorithm is proposed, so called ELSH (extended local sensitive hash), which can significantly improve matching accuracy at fast speed. Secondly, a novel unsupervised image grouping strategy is proposed to cluster the unordered images into content-related group, which does not normally require any other constraints. Extensive experimental results have shown that our method can obtain better performance than the classical algorithms in tackling multi-view matching problem.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
Pages690-693
Number of pages4
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Multimedia and Expo, ICME 2009 - New York, NY, United States
Duration: 28 Jun 20093 Jul 2009

Publication series

NameProceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009

Conference

Conference2009 IEEE International Conference on Multimedia and Expo, ICME 2009
Country/TerritoryUnited States
CityNew York, NY
Period28/06/093/07/09

Keywords

  • Extended LSH
  • Image grouping
  • KNN (K nearest neighbor) search
  • Multi-view matching

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