MIFT: A Moment-Based Local Feature Extraction Algorithm

We propose a local feature descriptor based on moment. Although conventional scale invariant feature transform (SIFT)-based algorithms generally use difference of Gaussian (DoG) for feature extraction, they remain sensitive to more complicated deformations. To solve this problem, we propose MIFT, an...

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Bibliographic Details
Published inApplied sciences Vol. 9; no. 7; p. 1503
Main Authors Zhang, Hua-Zhen, Kim, Dong-Won, Kang, Tae-Koo, Lim, Myo-Taeg
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2019
Subjects
Algorithms
feature extraction
Fuzzy sets
Histograms
Humanoid
Localization
MDGHM
Methods
Pattern recognition
Principal components analysis
Pyramids
Robots
SIFT
United States > US
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Summary:We propose a local feature descriptor based on moment. Although conventional scale invariant feature transform (SIFT)-based algorithms generally use difference of Gaussian (DoG) for feature extraction, they remain sensitive to more complicated deformations. To solve this problem, we propose MIFT, an invariant feature transform algorithm based on the modified discrete Gaussian-Hermite moment (MDGHM). Taking advantage of MDGHM’s high performance to represent image information, MIFT uses an MDGHM-based pyramid for feature extraction, which can extract more distinctive extrema than the DoG, and MDGHM-based magnitude and orientation for feature description. We compared the proposed MIFT method performance with current best practice methods for six image deformation types, and confirmed that MIFT matching accuracy was superior of other SIFT-based methods.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9071503