  This paper explores the use of the Artificial Bee Colony (ABC) algorithm to
compute threshold selection for image segmentation. ABC is a heuristic
algorithm motivated by the intelligent behavior of honey-bees which has been
successfully employed to solve complex optimization problems. In this approach,
an image 1D histogram is approximated through a Gaussian mixture model whose
parameters are calculated by the ABC algorithm. For the approximation scheme,
each Gaussian function represents a pixel class and therefore a threshold.
Unlike the Expectation Maximization (EM) algorithm, the ABC based method shows
fast convergence and low sensitivity to initial conditions. Remarkably, it also
improves complex time consuming computations commonly required by
gradient-based methods. Experimental results demonstrate the algorithms ability
to perform automatic multi threshold selection yet showing interesting
advantages by comparison to other well known algorithms.
