MIFT: A moment-based local feature extraction algorithm

Hua Zhen Zhang, Dong Won Kim, Tae Koo Kang, Myo Taeg Lim

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Article number1503
JournalApplied Sciences (Switzerland)
Issue number7
Publication statusPublished - 2019


  • Feature extraction
  • SIFT

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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