KAZE Features

  • Pablo Fernández Alcantarilla
  • Adrien Bartoli
  • Andrew J. Davison
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)


In this paper, we introduce KAZE features, a novel multiscale 2D feature detection and description algorithm in nonlinear scale spaces. Previous approaches detect and describe features at different scale levels by building or approximating the Gaussian scale space of an image. However, Gaussian blurring does not respect the natural boundaries of objects and smoothes to the same degree both details and noise, reducing localization accuracy and distinctiveness. In contrast, we detect and describe 2D features in a nonlinear scale space by means of nonlinear diffusion filtering. In this way, we can make blurring locally adaptive to the image data, reducing noise but retaining object boundaries, obtaining superior localization accuracy and distinctiviness. The nonlinear scale space is built using efficient Additive Operator Splitting (AOS) techniques and variable conductance diffusion. We present an extensive evaluation on benchmark datasets and a practical matching application on deformable surfaces. Even though our features are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space, but comparable to SIFT, our results reveal a step forward in performance both in detection and description against previous state-of-the-art methods.


Feature Detection Scale Space Scale Level Scale Invariant Feature Transform Nonlinear Diffusion 
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 2012

Authors and Affiliations

  • Pablo Fernández Alcantarilla
    • 1
  • Adrien Bartoli
    • 1
  • Andrew J. Davison
    • 2
  1. 1.ISIT-UMR 6284 CNRSUniversité d’AuvergneClermont FerrandFrance
  2. 2.Department of ComputingImperial College LondonUK

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