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This paper explores the use of the Artificial Bee Colony (ABC) algorithm to compute threshold selection for image segmentation. ABC is an evolutionary algorithm inspired by the intelligent behavior of honey-bees which has been successfully employed to solve complex optimization problems. In this approach, an image 1-D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. In the model, each Gaussian function represents a pixel class and therefore a threshold point. Unlike the Expectation-Maximization (EM) algorithm, the ABC 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 over multiple images with different range of complexity validate the efficiency of the proposed technique with regard to segmentation accuracy, speed, and robustness. The paper also includes an experimental comparison to the EM and to one gradient-based method which ultimately demonstrates a better performance from the proposed algorithm.

A multi-threshold segmentation approach based on Artificial Bee Colony optimization