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

Abstract EN

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.