Description of interest regions with center-symmetric local binary patterns

  • Authors:

  • Marko Heikkilä;Matti Pietikäinen;Cordelia Schmid

  • Affiliations:

  • Machine Vision Group, Infotech Oulu and Department of Electrical and Information Engineering, University of Oulu, Finland;Machine Vision Group, Infotech Oulu and Department of Electrical and Information Engineering, University of Oulu, Finland;INRIA Rhône-Alpes, Montbonnot, France

  • Venue:
  • ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
  • Year:
  • 2006

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Abstract

Local feature detection and description have gained a lot of interest in recent years since photometric descriptors computed for interest regions have proven to be very successful in many applications. In this paper, we propose a novel interest region descriptor which combines the strengths of the well-known SIFT descriptor and the LBP texture operator. It is called the center-symmetric local binary pattern (CS-LBP) descriptor. This new descriptor has several advantages such as tolerance to illumination changes, robustness on flat image areas, and computational efficiency. We evaluate our descriptor using a recently presented test protocol. Experimental results show that the CS-LBP descriptor outperforms the SIFT descriptor for most of the test cases, especially for images with severe illumination variations.