A fast recursive algorithm based on fuzzy 2-partition entropy approach for threshold selection
A fast recursive algorithm based on fuzzy 2-partition entropy approach for threshold selection
复制标题
一种基于模糊二划分熵方法的快速递归阈值选择算法
DOI：
10.1016/j.neucom.2011.04.010
复制
发表时间：
2011-10
期刊：
影响因子：
6
通讯作者：
中科院分区：
文献类型：
--
作者：
模糊c-划分熵阈值选择方法是一种有效的图像分割方法。该方法模型的图像与模糊c-分区，这是使用参数化的隶属函数。通过搜索隶属函数的最优参数组合来确定理想阈值，使得模糊c-划分的熵最大化。当确定隶属函数所需的参数数量增加时，它涉及大量的计算。本文提出了一种模糊二划分熵方法的递推算法，其中隶属函数选用三参数的S函数和Z函数。所提出的递归算法消除了许多重复计算，从而大大降低了计算复杂度。用几幅真实的图像对该方法进行了测试，并将其处理时间与基本穷举算法、遗传算法（GA）、粒子群优化算法（PSO）、蚁群优化算法（ACO）和模拟退火算法（SA）进行了比较。实验结果表明，该方法比基本的穷举搜索算法、遗传算法、粒子群算法、蚁群算法和模拟退火算法更有效。
The fuzzy c-partition entropy approach for threshold selection is an effective approach for image segmentation. The approach models the image with a fuzzy c-partition, which is obtained using parameterized membership functions. The ideal threshold is determined by searching an optimal parameter combination of the membership functions such that the entropy of the fuzzy c-partition is maximized. It involves large computation when the number of parameters needed to determine the membership function increases. In this paper, a recursive algorithm is proposed for fuzzy 2-partition entropy method, where the membership function is selected as S-function and Z-function with three parameters. The proposed recursive algorithm eliminates many repeated computations, thereby reducing the computation complexity significantly. The proposed method is tested using several real images, and its processing time is compared with those of basic exhaustive algorithm, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and simulated annealing (SA). Experimental results show that the proposed method is more effective than basic exhaustive search algorithm, GA, PSO, ACO and SA.
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影响因子：
5.1
作者：
Jin, Hai;Liu, Liman;Tao, Wenbing
通讯作者：
Tao, Wenbing
影响因子：
8
作者：
KITTLER, J;ILLINGWORTH, J
通讯作者：
ILLINGWORTH, J
影响因子：
8
作者：
Louloudis, G.;Gatos, B.;Halatsis, C.
通讯作者：
Halatsis, C.
影响因子：
3.9
作者：
通讯作者：
--
DOI：
--
发表时间：
2009-01
期刊：
--
影响因子：
--
作者：
M. Horng
通讯作者：
M. Horng