Multilevel thresholding segmentation based on harmony search optimization

Diego Oliva, Erik Cuevas, Gonzalo Pajares, Daniel Zaldivar, Marco Perez-Cisneros

Research output: Contribution to journalArticlepeer-review

100 Citations (Scopus)


In this paper, a multilevel thresholding (MT) algorithm based on the harmony search algorithm (HSA) is introduced. HSA is an evolutionary method which is inspired in musicians improvising new harmonies while playing. Different to other evolutionary algorithms, HSA exhibits interesting search capabilities still keeping a low computational overhead. The proposed algorithm encodes random samples from a feasible search space inside the image histogram as candidate solutions, whereas their quality is evaluated considering the objective functions that are employed by the Otsu's or Kapur's methods. Guided by these objective values, the set of candidate solutions are evolved through the HSA operators until an optimal solution is found. Experimental results demonstrate the high performance of the proposed method for the segmentation of digital images.

Original languageEnglish
Article number575414
JournalJournal of Applied Mathematics
Publication statusPublished - 2013
Externally publishedYes

ASJC Scopus subject areas

  • Applied Mathematics

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