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Statistical Modeling Based Adaptive Parameter Setting for Random Walk Segmentation

  • Ang Bian
  • Xiaoyi JiangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)

Abstract

Segmentation algorithms typically require some parameters and their optimal values are not easy to find. Training methods have been proposed to tune the optimal parameter values. In this work we follow an alternative goal of adaptive parameter setting. Considering the popular random walk segmentation algorithm it is demonstrated that the parameter used for the weighting function has a strong influence on the segmentation quality. We propose a statistical model based approach to automatically setting this parameter, thus adapting the segmentation algorithm to the statistic properties of an image. Experimental results are presented to demonstrate the usefulness of the proposed approach.

Keywords

Random Walk Image Segmentation Foreground Object Connected Node Segmentation Quality 
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

Ang Bian was supported by the China Scholarship Council (CSC). Xiaoyi Jiang was supported by the Deutsche Forschungsgemeinschaft (DFG): SFB656 MoBil (project B3) and EXC 1003 Cells in Motion – Cluster of Excellence.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of MünsterMünsterGermany

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