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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Multilevel thresholding selection based on the fireworks algorithm for image segmentation
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Multilevel thresholding selection based on the fireworks algorithm for image segmentation

机译：基于Fireworks算法的多级阈值选择算法。

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摘要

With the increasing number of the threshold, the computation of the multilevel minimum cross entropy thresholding will increase exponentially, and the processing efficiency will be low, thus it is difficult to be applied in real-time processing. Some classical optimization algorithms, such as genetic algorithm, particle swarm algorithm has been used to deal with such problems, but it is easy for them to fall into the local optimal solution, the performance is not robust. In this paper, we use the minimum cross entropy to define the objective function of the optimal image segmentation thresholding, solve the optimization problem with the new intelligence optimization algorithm-fireworks algorithm, and compare it with other algorithm. The experimental results show that the fireworks algorithm can solve the problem of multilevel thresholds image segmentation with minimum cross entropy, which is a promising multilevel thresholding method, and it is not easy to fall into local optimal solution.
机译：随着阈值数量的增加，多级最小交叉熵阈值的计算将成倍增加，处理效率较低，因此难以应用于实时处理。一些经典的优化算法，例如遗传算法，粒子群算法已经被用来解决这些问题，但是它们很容易陷入局部最优解，性能不强。本文使用最小交叉熵定义最优图像分割阈值的目标函数，用新的智能优化算法-烟花算法解决优化问题，并将其与其他算法进行比较。实验结果表明，该烟花算法能够以最小的交叉熵解决多级阈值图像分割问题，是一种很有前途的多级阈值化方法，不易陷入局部最优解。

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