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Non-convex Relaxation of Optimal Transport for Color Transfer Between Images

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Geometric Science of Information (GSI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9389))

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Abstract

Optimal transport (OT) is a major statistical tool to measure similarity between features or to match and average features. However, OT requires some relaxation and regularization to be robust to outliers. With relaxed methods, as one feature can be matched to several ones, important interpolations between different features arise. This is not an issue for comparison purposes, but it involves strong and unwanted smoothing for transfer applications. We thus introduce a new regularized method based on a non-convex formulation that minimizes transport dispersion by enforcing the one-to-one matching of features. The interest of the approach is demonstrated for color transfer purposes.

A preliminary version of this work has been presented at the NIPS 2014 Workshop on Optimal Transport and Machine Learning (pdf).

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Author information

Authors and Affiliations

  1. GREYC, UMR 6072, Université de Caen, 14050, Caen, France

    Julien Rabin

  2. CNRS, IMB, UMR 5251, 33400, Talence, France

    Nicolas Papadakis

Authors
  1. Julien Rabin
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  2. Nicolas Papadakis
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Corresponding author

Correspondence to Nicolas Papadakis .

Editor information

Editors and Affiliations

  1. Bâtiment Alan Turing, CS35003, École Polytechnique, Palaiseau, France

    Frank Nielsen

  2. Thales Land\& Air Systems, Limours, France

    Frédéric Barbaresco

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Rabin, J., Papadakis, N. (2015). Non-convex Relaxation of Optimal Transport for Color Transfer Between Images. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2015. Lecture Notes in Computer Science(), vol 9389. Springer, Cham. https://doi.org/10.1007/978-3-319-25040-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-25040-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25039-7

  • Online ISBN: 978-3-319-25040-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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