Progressive feature matching with alternate descriptor selection and correspondence enrichment

Yuan Ting Hu, Yen-Yu Lin

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

15 Scopus citations

Abstract

We address two difficulties in establishing an accurate system for image matching. First, image matching relies on the descriptor for feature extraction, but the optimal descriptor often varies from image to image, or even patch to patch. Second, conventional matching approaches carry out geometric checking on a small set of correspondence candidates due to the concern of efficiency. It may result in restricted performance in recall. We aim at tackling the two issues by integrating adaptive descriptor selection and progressive candidate enrichment into image matching. We consider that the two integrated components are complementary: The high-quality matching yielded by adaptively selected descriptors helps in exploring more plausible candidates, while the enriched candidate set serves as a better reference for descriptor selection. It motivates us to formulate image matching as a joint optimization problem, in which adaptive descriptor selection and progressive correspondence enrichment are alternately conducted. Our approach is comprehensively evaluated and compared with the state-of-the-art approaches on two benchmarks. The promising results manifest its effectiveness.

Original languageEnglish
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
PublisherIEEE Computer Society
Pages346-354
Number of pages9
ISBN (Electronic)9781467388504
DOIs
StatePublished - 9 Dec 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States
Duration: 26 Jun 20161 Jul 2016

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2016-December
ISSN (Print)1063-6919

Conference

Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
CountryUnited States
CityLas Vegas
Period26/06/161/07/16

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