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

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  • Han Wang
  • Zhihuo Xu
  • Hanseok Ko

Abstract

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.

Suggested Citation

  • Han Wang & Zhihuo Xu & Hanseok Ko, 2018. "Random Binary Local Patch Clustering Transforms Based Image Matching for Nonlinear Intensity Changes," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-16, September.
    • Handle: RePEc:hin:jnlmpe:6360741
    DOI: 10.1155/2018/6360741
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