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Locality Preserving Matching

Authors
  • Ma, Jiayi1
  • Zhao, Ji2
  • Jiang, Junjun3
  • Zhou, Huabing4
  • Guo, Xiaojie5
  • 1 Wuhan University, Electronic Information School, Wuhan, 430072, China , Wuhan (China)
  • 2 ReadSense Ltd., Shanghai, 200040, China , Shanghai (China)
  • 3 Harbin Institute of Technology, School of Computer Science and Technology, Harbin, 150001, China , Harbin (China)
  • 4 Wuhan Institute of Technology, Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan, 430073, China , Wuhan (China)
  • 5 Tianjin University, School of Computer Software, Tianjin, 300350, China , Tianjin (China)
Type
Published Article
Journal
International Journal of Computer Vision
Publisher
Springer-Verlag
Publication Date
Sep 22, 2018
Volume
127
Issue
5
Pages
512–531
Identifiers
DOI: 10.1007/s11263-018-1117-z
Source
Springer Nature
Keywords
License
Yellow

Abstract

Seeking reliable correspondences between two feature sets is a fundamental and important task in computer vision. This paper attempts to remove mismatches from given putative image feature correspondences. To achieve the goal, an efficient approach, termed as locality preserving matching (LPM), is designed, the principle of which is to maintain the local neighborhood structures of those potential true matches. We formulate the problem into a mathematical model, and derive a closed-form solution with linearithmic time and linear space complexities. Our method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds. To demonstrate the generality of our strategy for handling image matching problems, extensive experiments on various real image pairs for general feature matching, as well as for point set registration, visual homing and near-duplicate image retrieval are conducted. Compared with other state-of-the-art alternatives, our LPM achieves better or favorably competitive performance in accuracy while intensively cutting time cost by more than two orders of magnitude.

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