Random Binary Local Patch Clustering Transforms Based Image Matching for Nonlinear Intensity Changes

Han Wang, Zhihuo Xu, Hanseok Ko

Research output: Contribution to journalArticle


This paper presents a new feature descriptor that is suitable for image matching under nonlinear intensity changes. The proposed approach consists of the following three steps. First, a binary local patch clustering transform response is employed as the transform space. The value of the new space exhibits a high similarity after changes in intensity. Then, a random binary pattern coding method extracts raw feature histograms from the new space. Finally, the discrimination of the proposed feature descriptor is enhanced by using a multiple spatial support region-based binning method. Experimental results show that the proposed method is able to provide a more robust image matching performance under nonlinear intensity changes.

Original languageEnglish
Article number6360741
JournalMathematical Problems in Engineering
Publication statusPublished - 2018 Jan 1


ASJC Scopus subject areas

  • Mathematics(all)
  • Engineering(all)

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