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Image Histogram Constrained SIFT Matching

  • Ye Luo
  • Ping Xue
  • Qi Tian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)

Abstract

Scale Invariant Feature Transform (SIFT) is a powerful tool in image/object matching and recognition. However, with its local nature, global information of images, such as the histogram, is ignored in its original formulation. Since histogram matching is almost a necessary condition for a pair of matching images, such ignorance can be problematic especially when SIFT is used for matching images/scenes. In this paper we propose a novel method based on making use of both SIFT features and the local intensity histograms on the feature points in order to achieve more robust image matching. And many false matches can be rejected by the proposed method. Experimental results on natural scene matching and image retrieval have showed the efficiency of the proposed approach.

Keywords

Feature Point Image Retrieval Scale Invariant Feature Transform Image Match Local Histogram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ye Luo
    • 1
  • Ping Xue
    • 1
  • Qi Tian
    • 2
  1. 1.School of EEENanyang Technological UniversitySingapore
  2. 2.Institute for Infocomm ResearchSingapore

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