This chapter explores the use of the Artificial Bee Colony (ABC) algorithm to compute pixel classification for image segmentation. ABC is a heuristic algorithm motivated by the intelligent behaviour 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. 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 algorithm's ability to perform automatic multi-threshold selection yet showing interesting advantages by comparison to other well-known algorithms. © Springer-Verlag Berlin Heidelberg 2013.
CITATION STYLE
Cuevas, E., Sención-Echauri, F., Zaldivar, D., & Pérez, M. (2013). Image Segmentation Using Artificial Bee Colony Optimization. Intelligent Systems Reference Library, 38, 965–990. https://doi.org/10.1007/978-3-642-30504-7_38
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