Multilevel Thresholding Segmentation Based on Harmony Search Optimization

An implementation of harmony search algorithm for multilevel thresholding image segmentation

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.
****The main file for each method (OTSU or KAPUR) is Mth.HS1.m****
The proposed algorithm was published in:
Diego Oliva, Erik Cuevas, Gonzalo Pajares, Daniel Zaldivar, and Marco Perez-Cisneros, “Multilevel Thresholding Segmentation Based on Harmony Search Optimization,” Journal of Applied Mathematics, vol. 2013, Article ID 575414, 24 pages, 2013. doi:10.1155/2013/575414
Journal's download link:
http://www.hindawi.com/journals/jam/2013/575414/