Robust feature matching via multiple descriptor fusion

Yuan Ting Hu, Yen-Yu Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a novel approach to boost image matching performance by fusing multiple local descriptors in the homography space. Traditional matching methods find correspondences based on a single descriptor and the performance becomes unstable due to the goodness of the chosen descriptor To address this problem, our method uses multiple descriptors and select a good descriptor for matching each feature point. Specifically, we project every correspondence into the homography space, where correct correspondences tend to gather together due to the similarity of their homographies. Then kernel density estimation is applied to measure the density in the homography space and verify the correctness of correspondences. The proposed approach is comprehensively compared with the state-of-the-art methods and the promising results manifest its effectiveness.

Original languageEnglish
Title of host publicationProceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages271-275
Number of pages5
ISBN (Electronic)9781479961009
DOIs
StatePublished - 7 Jun 2016
Event3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 - Kuala Lumpur, Malaysia
Duration: 3 Nov 20166 Nov 2016

Publication series

NameProceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015

Conference

Conference3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
CountryMalaysia
CityKuala Lumpur
Period3/11/166/11/16

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