• DocumentCode
    3579933
  • Title

    Contextual descriptors improving credibility of keypoint matching: Harris-Affine, Hessian-Affme and SIFT feasibility study

  • Author

    Sluzek, Andrzej

  • Author_Institution
    ECE Dept., Khalifa Univ., United Arab Emirates
  • fYear
    2014
  • Firstpage
    117
  • Lastpage
    122
  • Abstract
    The paper proposes CONSIFT descriptors which are the rotation-variant modification of SIFT (primarily for affine-invariant keypoints). CONSIFT of a keypoint K is its SIFT computed relatively to the orientation defined by the location of another keypoint L (and concatenated with similarly computed SIFT for keypoint L relatively to the location of K). It is additionally recommended that K and L are extracted by different detectors of complementary properties (e.g. Harris-Affine and Hessian-Affine). The paper evaluates CONSIFT performances over the benchmark dataset. Surprisingly, performances of CONSIFT are nearly equivalent to SIFT. However, the intersection of SIFT-based and CONSIFT-based matches is a highly discriminative tool which rejects most of correspondences which are incorrect in a wider context. Since the method does not require any consistency verification over groups of preliminary matched keypoints (and its memory usage is acceptable) it can be instrumental in large-scale CBVIR systems.
  • Keywords
    Hessian matrices; affine transforms; content-based retrieval; feature extraction; image matching; image retrieval; vocabulary; CONSIFT descriptors; CONSIFT-based matches; Hessian-Affine transform; affine-invariant keypoints; benchmark dataset; consistency verification; contextual descriptors; keypoint matching credibility; large-scale CBVIR systems; memory usage; rotation-variant modification; Benchmark testing; Context; Detectors; Image retrieval; Vectors; Visualization; Vocabulary; SIFT; affine invariance; keypoint matching; near-duplicate images and sub-images; visual words;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on
  • Type

    conf

  • DOI
    10.1109/ICARCV.2014.7064290
  • Filename
    7064290