Multi-threshold Image Segmentation Based on Firefly Algorithm

Chao Yu, Bi Jin, Y. Lu, Xiwei Chen, Zhengming Yi, Kai Zhang, Shaoliang Wang
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引用次数: 13

Abstract

Multi-threshold image segmentation have achieved good results, but multi-threshold searching can be very time consuming, in order to improve the efficiency of multi-threshold searching, in this paper, the Firefly Algorithm is applied to solve multi-threshold searching. With maximum entropy as the optimization objective function, more information of images can be reserved, then using the Firefly Algorithm's heuristic searching, the optimal thresholds of image segmentation can be found. Experimental results show that compared to Genetic Algorithm, our proposed Firefly Algorithm has a higher convergence speed and produces a better segmentation effect when applied to image segmentation.
基于Firefly算法的多阈值图像分割
多阈值图像分割已经取得了很好的效果,但是多阈值搜索非常耗时,为了提高多阈值搜索的效率,本文采用萤火虫算法求解多阈值搜索。以最大熵为优化目标函数,保留更多的图像信息,利用萤火虫算法的启发式搜索,找到图像分割的最优阈值。实验结果表明,与遗传算法相比,我们提出的萤火虫算法具有更快的收敛速度和更好的图像分割效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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