Building kernels from binary strings for image matching

  • Odone F
  • Barla A
  • Verri A
  • 37

    Readers

    Mendeley users who have this article in their library.
  • 88

    Citations

    Citations of this article.

Abstract

In the statistical learning framework, the use of appropriate kernels may be the key for substantial improvement in solving a given problem. In essence, a kernel is a similarity measure between input points satisfying some mathematical requirements and possibly capturing the domain knowledge. In this paper, we focus on kernels for images: we represent the image information content with binary strings and discuss various bitwise manipulations obtained using logical operators and convolution with nonbinary stencils. In the theoretical contribution of our work, we show that histogram intersection is a Mercer's kernel and we determine the modifications under which a similarity measure based on the notion of Hausdorff distance is also a Mercer's kernel. In both cases, we determine explicitly the mapping from input to feature space. The presented experimental results support the relevance of our analysis for developing effective trainable systems.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Francesca Odone

  • Annalisa Barla

  • Alessandro Verri

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free