Color image segmentation by minimal surface smoothing

Zhi Li*, Tieyong ZENG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition - 10th International Conference,EMMCVPR 2015, Proceedings
EditorsXue-Cheng Tai, Egil Bae, Tony F. Chan, Marius Lysaker
PublisherSpringer Verlag
Pages321-334
Number of pages14
ISBN (Electronic)9783319146119
DOIs
Publication statusPublished - 2015
Event10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2015 - Hong Kong, China
Duration: 13 Jan 201516 Jan 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8932
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2015
Country/TerritoryChina
CityHong Kong
Period13/01/1516/01/15

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Image segmentation
  • Minimal surface
  • Primal-dual method
  • Total variation

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