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Patch Matching with Polynomial Exponential Families and Projective Divergences

  • Frank NielsenEmail author
  • Richard Nock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9939)

Abstract

Given a query patch image, patch matching consists in finding similar patches in a target image. In pattern recognition, patch matching is a fundamental task that is time consuming, specially when zoom factors and symmetries are handled. The matching results heavily depend on the underlying notion of distances, or similarities, between patches. We present a method that consists in modeling patches by flexible statistical parametric distributions called polynomial exponential families (PEFs). PEFs model universally arbitrary smooth distributions, and yield a compact patch representation of complexity independent of the patch sizes. Since PEFs have computationally intractable normalization terms, we estimate PEFs with score matching, and consider a projective distance: the symmetrized \(\gamma \)-divergence. We demonstrate experimentally the performance of our patch matching system.

Keywords

Patch Size Projective Divergence Target Image Exponential Family Natural Parameter 
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.

Notes

Acknowledgments

We gratefully thank Quei-An Chen (École Polytechnique, France) for implementing our patch matching system and performing various experiments.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.École PolytechniquePalaiseauFrance
  2. 2.Sony Computer Science Laboratories Inc.TokyoJapan
  3. 3.Data61Sydney ATPSydneyAustralia
  4. 4.The Australian National UniversitySydneyAustralia

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