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Journal of Applied Mathematics
Volume 2013, Article ID 575414, 24 pages
Research Article

Multilevel Thresholding Segmentation Based on Harmony Search Optimization

1Departamento de Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense, 28040 Madrid, Spain
2Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, CU-TONALA, Avenida Revolución 1500, C.P 44430, Guadalajara, JAL, Mexico

Received 14 June 2013; Revised 17 August 2013; Accepted 20 August 2013

Academic Editor: Zong Woo Geem

Copyright © 2013 Diego Oliva et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.