An image matching optimization algorithm based on pixel shift clustering RANSAC

Shuhua Ma, Peikai Guo, Hairong You, Ping He, Guanglin Li, Heng Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)


This paper focuses on improving the accuracy of image matching by eliminating the residual mismatches in the matching results of standard RANSAC. Based on pixel shift clustering and RANSAC algorithms, a matching optimization algorithm called pixel shift clustering RANSAC, PSC-RANSAC in short, is proposed in this paper. Firstly, the pixel shift model of space point from two perspectives are established by parallax principle and camera projection model. Then, based on the established pixel shift model, density peaks clustering (DPC) algorithm is used to select the mismatches out to enhance the accuracy of image matching. Meanwhile the comparisons among PSC-RANSAC, standard RANSAC, progressive sample consensus and graph-cut RANSAC show that PSC-RANSAC can more effectively and robustly eliminate the residual mismatches in initial matching results. The proposed method provides an effective tool for optimization on image matching.

Original languageEnglish
Pages (from-to)452-474
Number of pages23
JournalInformation Sciences
Publication statusPublished - Jul 2021


  • Clustering
  • Image matching
  • Pixel shift

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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