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Color Image Segmentation by Minimal Surface Smoothing

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8932))

Abstract

In this paper, we propose a two-stage approach for color image segmentation, which is inspired by minimal surface smoothing. Indeed, the first stage is to find a smooth solution to a convex variational model related to minimal surface smoothing. The classical primal-dual algorithm can be applied to efficiently solve the minimization problem. Once the smoothed image u is obtained, in the second stage, the segmentation is done by thresholding. Here, instead of using the classical K-means to find the thresholds, we propose a hill-climbing procedure to find the peaks on the histogram of u, which can be used to determine the required thresholds. The benefit of such approach is that it is more stable and can find the number of segments automatically. Finally, the experiment results illustrate that the proposed algorithm is very robust to noise and exhibits superior performance for color image segmentation.

This work is supported in part by the National Science Foundation of China (11271049), by HKRGC 211710 and 211911, by the FRGs of Hong Kong Baptist University, and by the HKPFS from HKRGC.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Hong Kong Baptist University, Hong Kong, China

    Zhi Li & Tieyong Zeng

Authors
  1. Zhi Li
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  2. Tieyong Zeng
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Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Bergen, 7800, Bergen, Norway

    Xue-Cheng Tai

  2. Department of Mathematics, University of California, Los Angeles, CA, USA

    Egil Bae

  3. The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, S.A.R.

    Tony F. Chan

  4. Telemark University College, Postboks 203, 3901, Porsgrunn, Norway

    Marius Lysaker

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Li, Z., Zeng, T. (2015). Color Image Segmentation by Minimal Surface Smoothing. In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, Cham. https://doi.org/10.1007/978-3-319-14612-6_24

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  • DOI: https://doi.org/10.1007/978-3-319-14612-6_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14611-9

  • Online ISBN: 978-3-319-14612-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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