A genetic algorithm for MRF-based segmentation of multi-spectral textured images

Din Chang Tseng, Chih Ching Lai

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


A segmentation approach based on a Markov random field (MRF) model is an iterative algorithm; it needs many iteration steps to approximate a near optimal solution or gets a non-suitable solution with a few iteration steps. In this paper, we use a genetic algorithm (GA) to improve an unsupervised MRF-based segmentation approach for multi-spectral textured images. The proposed hybrid approach has the advantage that combines the fast convergence of the MRF-based iterative algorithm and the powerful global exploration of the GA. In experiments, synthesized color textured images and multi-spectral remote-sensing images were processed by the proposed approach to evaluate the segmentation performance. The experimental results reveal the proposed approach really improves the MRF-based segmentation for the multi-spectral textured images.

Original languageEnglish
Pages (from-to)1499-1510
Number of pages12
JournalPattern Recognition Letters
Issue number14
StatePublished - Dec 1999


  • Genetic algorithm
  • Markov random field
  • Multi-spectral remote-sensing images
  • Unsupervised texture segmentation


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