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This paper explores the use of the Learning Automata (LA) algorithm tocompute threshold selection for image segmentation as it is a criticalpreprocessing step for image analysis, pattern recognition and computer vision.LA is a heuristic method which is able to solve complex optimization problemswith interesting results in parameter estimation. Despite other techniquescommonly seek through the parameter map, LA explores in the probability spaceproviding appropriate convergence properties and robustness. The segmentationtask is therefore considered as an optimization problem and the LA is used togenerate the image multi-threshold separation. In this approach, one 1Dhistogram of a given image is approximated through a Gaussian mixture modelwhose parameters are calculated using the LA algorithm. Each Gaussian functionapproximating the histogram represents a pixel class and therefore a thresholdpoint. The method shows fast convergence avoiding the typical sensitivity toinitial conditions such as the Expectation Maximization (EM) algorithm or thecomplex time-consuming computations commonly found in gradient methods.Experimental results demonstrate the algorithm ability to perform automaticmulti-threshold selection and show interesting advantages as it is compared toother algorithms solving the same task.

Seeking multi-thresholds for image segmentation with Learning Automata