Abstract
Evolutionary algorithms are used in many engineering applications for optimization of problems that are often difficult to solve using conventional methods. One such problem is image segmentation. This task is used for object (contour) extraction from images to create sensible representation of the image. There are many image segmentation and optimization methods. This work is focused on selected evolutionary optimization methods. Namely, particle swarm optimization, genetic algorithm, and differential evolution. Our image segmentation method is inspired in algorithm known as k-means. The optimization function from k-means algorithm is replaced by evolutionary technique. We compare original k-means algorithm with evolutionary approaches and we show that our evolutionary approaches easily outperform the classical approach.
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© 2014 Springer International Publishing Switzerland
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Mozdren, K., Burianek, T., Platos, J., Snášel, V. (2014). Evolutionary Techniques for Image Segmentation. In: Kömer, P., Abraham, A., Snášel, V. (eds) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Advances in Intelligent Systems and Computing, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-319-08156-4_29
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DOI: https://doi.org/10.1007/978-3-319-08156-4_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08155-7
Online ISBN: 978-3-319-08156-4
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