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Stereo matching-based definition of saliency via sample-based Kullback–Leibler divergence estimation

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Abstract

The saliency of a given image region for stereo matching is measured by the Kullback–Leibler divergence from the conditional probability density function (pdf) for the matching region to a pdf for the background. If the region has a high saliency, then there is a high probability that the correct matching region can be found. The Kullback–Leibler divergence is directly estimated from the samples without an intermediate estimation of the pdfs. Experiments with the stereo images in the Middlebury database show that the proposed estimation of the Kullback–Leibler divergence is faster than the estimation based on parametric models for the pdfs, with no loss of accuracy.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072, PR China

    Jun Liang & Yanning Zhang

  2. Department of Computer Science and Information Systems, Birkbeck College, University of London, London, UK

    Jun Liang & Stephen J. Maybank

Authors
  1. Jun Liang
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  2. Stephen J. Maybank
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  3. Yanning Zhang
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Correspondence to Yanning Zhang.

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Liang, J., Maybank, S.J. & Zhang, Y. Stereo matching-based definition of saliency via sample-based Kullback–Leibler divergence estimation. Machine Vision and Applications 26, 607–618 (2015). https://doi.org/10.1007/s00138-015-0685-y

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  • DOI: https://doi.org/10.1007/s00138-015-0685-y

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