Image Matching Using Generalized Scale-Space Interest Points

  • Tony Lindeberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7893)


The performance of matching and object recognition methods based on interest points depends on both the properties of the underlying interest points and the associated image descriptors. This paper demonstrates the advantages of using generalized scale-space interest point detectors when computing image descriptors for image-based matching. These generalized scale-space interest points are based on linking of image features over scale and scale selection by weighted averaging along feature trajectories over scale and allow for a higher ratio of correct matches and a lower ratio of false matches compared to previously known interest point detectors within the same class. Specifically, it is shown how a significant increase in matching performance can be obtained in relation to the underlying interest point detectors in the SIFT and the SURF operators. We propose that these generalized scale-space interest points when accompanied by associated scale-invariant image descriptors should allow for better performance of interest point based methods for image-based matching, object recognition and related vision tasks.


interest points scale selection scale linking matching object recognition feature detection scale invariance scale space 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tony Lindeberg
    • 1
  1. 1.School of Computer Science and CommunicationKTH Royal Institute of TechnologyStockholmSweden

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