  Segmentation is one of the most important tasks in image processing. It
consist in classify the pixels into two or more groups depending on their
intensity levels and a threshold value. The quality of the segmentation depends
on the method applied to select the threshold. The use of the classical
implementations for multilevel thresholding is computationally expensive since
they exhaustively search the best values to optimize the objective function.
Under such conditions, the use of optimization evolutionary approaches has been
extended. The Electromagnetism Like algorithm (EMO) is an evolutionary method
which mimics the attraction repulsion mechanism among charges to evolve the
members of a population. Different to other algorithms, EMO exhibits
interesting search capabilities whereas maintains a low computational overhead.
In this paper, a multilevel thresholding (MT) algorithm based on the EMO is
introduced. The approach combines the good search capabilities of EMO algorithm
with objective functions proposed by the popular MT methods of Otsu and Kapur.
The algorithm takes random samples from a feasible search space inside the
image histogram. Such samples build each particle in the EMO context whereas
its quality is evaluated considering the objective that is function employed by
the Otsu or Kapur method. Guided by these objective values the set of candidate
solutions are evolved through the EMO operators until an optimal solution is
found. The approach generates a multilevel segmentation algorithm which can
effectively identify the threshold values of a digital image in a reduced
number of iterations. Experimental results show performance evidence of the
implementation of EMO for digital image segmentation.
