  This paper explores the use of the Learning Automata (LA) algorithm to
compute threshold selection for image segmentation as it is a critical
preprocessing step for image analysis, pattern recognition and computer vision.
LA is a heuristic method which is able to solve complex optimization problems
with interesting results in parameter estimation. Despite other techniques
commonly seek through the parameter map, LA explores in the probability space
providing appropriate convergence properties and robustness. The segmentation
task is therefore considered as an optimization problem and the LA is used to
generate the image multi-threshold separation. In this approach, one 1D
histogram of a given image is approximated through a Gaussian mixture model
whose parameters are calculated using the LA algorithm. Each Gaussian function
approximating the histogram represents a pixel class and therefore a threshold
point. The method shows fast convergence avoiding the typical sensitivity to
initial conditions such as the Expectation Maximization (EM) algorithm or the
complex time-consuming computations commonly found in gradient methods.
Experimental results demonstrate the algorithm ability to perform automatic
multi-threshold selection and show interesting advantages as it is compared to
other algorithms solving the same task.
