An improvement to the SIFT descriptor for image representation and matching

  • Authors:
  • Kaiyang Liao;Guizhong Liu;Youshi Hui

  • Affiliations:
  • -;-;-

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2013

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Abstract

Constructing proper descriptors for interest points in images is a critical aspect for local features related tasks in computer vision and pattern recognition. Although the SIFT descriptor has been proven to perform better than the other existing local descriptors, it does not gain sufficient distinctiveness and robustness in image match especially in the case of affine and mirror transformations, in which many mismatches could occur. This paper presents an improvement to the SIFT descriptor for image matching and retrieval. The framework of the proposed descriptor consists of the following steps: normalizing elliptical neighboring region, transforming to affine scale-space, improving the SIFT descriptor with polar histogram orientation bin, as well as integrating the mirror reflection invariant. A comparative evaluation of different descriptors is carried out showing that the present approach provides better results than the existing methods.