Deformable template matching using proposal-based best-buddies similarity

Haiying Xia, Wenxian Zhao, Zheng Zhou, Frank Jiang, Haisheng Li, Xiangjian He

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

Abstract

We propose a new method for template matching based on the Best-Buddies Similarity (BBS) measure. Our method is able to match objects with large difference in size and hence achieves a deformable template matching. In addition, compared with the original method for template matching based on the BBS, our method significantly cuts down on the computation time. The fast and deformable template matching is implemented by measuring the BBS of only potential areas instead of all positions in an image. The potential areas, which can have different size from the given template, are found by a proposal generation based on edge priors and a selective search among the obtained proposals. The results from the experiments conduct-ed on a challenging dataset demonstrate that our method out-performs the state-of-the-art methods in terms of accuracy.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages517-521
Number of pages5
ISBN (Electronic)9781509049059
DOIs
Publication statusPublished - 7 Sep 2017
Externally publishedYes
Event16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017 - Sydney, Australia
Duration: 1 Aug 20174 Aug 2017

Publication series

NameProceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017

Conference

Conference16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017
Country/TerritoryAustralia
CitySydney
Period1/08/174/08/17

Keywords

  • Best Buddies Similarity
  • Deformable
  • Template matching

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Software
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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