PCNN文本图像分割的细菌觅食优化算法

PCNN image segmentation method based on bactrial foraging optimization algorithm

摘要:

为解决脉冲耦合神经网络(pulse-coupled neural network,PCNN)模型参数人工凭经验和需要反复实验才能确定的难题, 提出一种基于改进的PCNN模型. 以最大类间方差函数作为细菌觅食算法的适应度函数, 采用细菌觅食优化算法搜索最优参数的图像分割算法,避免了人工实验设定参数的盲目性．实验结果表明,该算法可以有效实现文本图像分割,并且分割效果明显优于对比算法．

Abstract:

To handle the difficult task of setting the relative parameters properly in the research and application of Pulse Coupled Neural Networks (PCNN), an improved PCNN algorithm is proposed. It uses the maximum between-cluster variance function as the fitness function of bacterial foraging optimization algorithm, and adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually setting the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document image segmentation, and result of the segmentation is obviously better than the contrast algorithms.