Multilevel Thresholding for Image Segmentation using the Galaxy-based Search Algorithm

Автор: Hamed Shah-Hosseini

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 11 vol.5, 2013 года.

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In this paper, image segmentation of gray-level images is performed by multilevel thresholding. The optimal thresholds for this purpose are found by maximizing the between-class variance (the Otsu’s criterion). The optimization (maximization) is conducted by a novel nature-inspired search algorithm, which is called Galaxy-based Search Algorithm or GbSA. The proposed GbSA is a metaheuristic for continuous optimization. It resembles the spiral arms of some galaxies to search for the optimal thresholds. The GbSA also uses a modified Hill Climbing algorithm as a local search. The GbSA also utilizes chaos for improving its performance, which is implemented by the logistic map. Experimental results show that the GbSA finds the optimal or very near optimal thresholds in all runs of the algorithm.

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Image Segmentation, Thresholding, Metaheuristic, Optimization, OTSU, Chaos

Короткий адрес: https://sciup.org/15010487

IDR: 15010487

Список литературы Multilevel Thresholding for Image Segmentation using the Galaxy-based Search Algorithm

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