Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.     

Using Disruptive Selection to Maintain Diversity in Genetic Algorithms

Genetic algorithms are a class of adaptive search techniques based on the principles of population genetics. The metaphor underlying genetic algorithms is that of natural evolution. With their great robustness, genetic algorithms have proven to be a promising technique for many optimization, design, control, and machine learning applications. A novel selection method, disruptive selection, has been proposed. This method adopts a nonmonotonic fitness function that is quite different from conventional monotonic fitness functions. Unlike conventional selection methods, this method favors both superior and inferior individuals. Since genetic algorithms allocate exponentially increasing numbers of trials to the observed better parts of the search space, it is difficult to maintain diversity in genetic algorithms. We show that Disruptive Genetic Algorithms (DGAs) effectively alleviate this problem by first demonstrating that DGAs can be used to solve a nonstationary search problem, where the goal is to track time-varying optima. Conventional Genetic Algorithms (CGAs) using proportional selection fare poorly on nonstationary search problems because of their lack of population diversity after convergence. Experimental results show that DGAs immediately track the optimum after the change of environment. We then describe a spike function that causes CGAs to miss the optimum. Experimental results show that DGAs outperform CGAs in resolving a spike function.     

A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings

For the first time, a genetic framework using contextual knowledge is proposed for segmentation and recognition of unconstrained handwritten numeral strings. New algorithms have been developed to locate feature points on the string image, and to generate possible segmentation hypotheses. A genetic representation scheme is utilized to show the space of all segmentation hypotheses (chromosomes). For the evaluation of segmentation hypotheses, a novel evaluation scheme is introduced, in order to improve the outlier resistance of the system. Our genetic algorithm tries to search and evolve the population of segmentation hypotheses, and to find the one with the highest segmentation/recognition confidence. The NIST NSTRING SD19 and CENPARMI databases were used to evaluate the performance of our proposed method. Our experiments showed that proper use of contextual knowledge in segmentation, evaluation and search greatly improves the overall performance of the system. On average, our system was able to obtain correct recognition rates of 95.28% and 96.42% on handwritten numeral strings using neural network and support vector classifiers, respectively. These results compare favorably with the ones reported in the literature.     

Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm

In the paper, a three-level thresholding method for image segmentation is presented, based on probability partition, fuzzy partition and entropy theory. A new fuzzy entropy has been defined through probability analysis. The image is divided into three parts, namely, dark, gray and white part, whose member functions of the fuzzy region are Z-function and Π-function and S-function, respectively, while the width and attribute of the fuzzy region can be determined by maximizing fuzzy entropy. The procedure for finding the optimal combination of all the fuzzy parameters is implemented by a genetic algorithm with appropriate coding method so as to avoid useless chromosomes. The experiment results show that the proposed method gives good performance.     

基于遗传优化谱聚类的图形分割方法

传统的谱聚类方法使用k-means达到最后的聚类目的。k-means对初始条件敏感,易陷入局部最优,从而导致传统的谱聚类方法应用到图像分割时效果不太理想。将遗传算法用于优化谱方法的聚类阶段,提出一种以遗传算法优化普聚类的图像分割方法(Image Segmentation Algorithm of Spectral Clustering Optimization Based on Genetic,ISCOG)。在合成图像与真实图像上的实验表明ISCOG算法极大地提高了谱聚类算法的稳定性和聚类质量,证明了ISCOG算法的优越性。     

A hybrid evolutionary approach to segmentation of non-stationary signals

Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varri?s and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm.     

基于遗传策略的图像灰度多阈值选择方法

阈值法是图像处理中阈值选择的最重要方法之一,吸引了很多研究者的注意力。ChunDN等人将遗传算法引入到阈值选择问题中,提出了基于遗传算法的鲁棒图像分割准则。文中对遗传算法做了改进,提出了自己的图像分割方法,并针对多阈值选择中计算量太大的问题,提出了自己的算法。通过实验将该算法与模拟退火算法进行了比较,结果充分显示了算法的有效性。     

基于RGB彩色空间的图像分割研究

图像分割是图像处理中的主要问题,图像分割效果的好坏直接影响图像分析的结果。彩色图像分割是指将彩色图像分割成各具特性的区域并提取出其中感兴趣的目标,为后续图像处理工作奠定基础。针对彩色图像梯度图进行分水岭分割会造成过分割的问题,比较阈值分割、最大类间方差分割和最大熵分割等图像分割方法,提出一种基于遗传算法改进最大熵的彩色图像分割方法。实验结果表明,该图像分割算法灵活性强,可以有效地分割彩色图像。     

基于遗传算法和简化PCNN神经网络的裂缝检测方法*

为能够在复杂背景下检测裂缝、分析裂缝图像特征,由脉冲耦合神经网络(pulse coupled neural networks, PCNN)的运行特征和神经元的状态变化分析简化PCNN模型,将简化PCNN模型用于裂缝图像的目标检测。针对PCNN无法确定裂缝图像的最优检测以及脉冲门限具有非线性因子的问题,提出了一种基于遗传算法(genetic algorithm, GA)和简化PCNN的裂缝图像检测方法。该方法采用最小误差准则作为遗传算法的适应度函数,并且根据遗传算法具有全局最优解的特点确定简化PCNN中各因子的值,实现了简化PCNN的裂缝图像自动分割。将本文方法与不同的分割方法对实际裂缝图像的处理结果进行比较,通过区域对比度、准确率和召回率等客观指标进行定量分析,表明了该方法对裂缝图像检测的有效性与通用性。     

一种基于遗传算法的彩色图像分割改进算法

图像分割是进行图像理解的基础,也是图像工程技术中一个重要的问题.近几年来关于图像的分割方法层出不穷,但随着多媒体技术和Internet技术的发展,彩色图像分割处理的准确性和实时性要求也越来越高.为此提出了一种基于遗传算法的彩色图像分割改进算法.经过大量的对比实验表明,用这种方法分割目标和背景区域差别较大的彩色图像具有分割效果好、实时性、鲁棒性强的特点,是一种较为理想的分割方法.     

基于最大方差法和改进遗传算法的图像分割

针对应用标准遗传算法对一幅灰度图像寻找最优阈值时经常陷入局部寻优的问题,提出了一种利用最大方差法和新的改进遗传算法相结合对图像进行分割的方法.以灰度图像的最大方差作为适应度函数,把图像分割问题变成一个优化问题.利用遗传算法的寻优高效性,搜索到能使分割质量达到最优的分割阈值.实验结果表明,采用新的改进遗传算法和最大方差法相结合对图像搜索全局阈值时能收敛至全局最优解,并且大大缩短寻找最优阈值的时间.     

A novel PPGA-based clustering analysis method for business cycle indicator selection

A new clustering analysis method based on the pseudo parallel genetic algorithm (PPGA) is proposed for business cycle indicator selection. In the proposed method, the category of each indicator is coded by real numbers, and some illegal chromosomes are repaired by the identification and restoration of empty class. Two mutation operators, namely the discrete random mutation operator and the optimal direction mutation operator, are designed to balance the local convergence speed and the global convergence performance, which are then combined with migration strategy and insertion strategy. For the purpose of verification and illustration, the proposed method is compared with the K-means clustering algorithm and the standard genetic algorithms via a numerical simulation experiment. The experimental result shows the feasibility and effectiveness of the new PPGA-based clustering analysis algorithm. Meanwhile, the proposed clustering analysis algorithm is also applied to select the business cycle indicators to examine the status of the macro economy. Empirical results demonstrate that the proposed method can effectively and correctly select some leading indicators, coincident indicators, and lagging indicators to reflect the business cycle, which is extremely operational for some macro economy administrative managers and business decision-makers.     

一种基于融合技术的成捆棒材端面图象的分割方案

针对特定工业现场环境下成捆棒材端面图象的分割问题,提出了一种基于融合技术的图象分割方案.该方案先分别采用阈值化与基于边界的分割方法对图象进行初始分割,然后将所得数据加以关联,并采用遗传算法加以融合与提取.实验结果表明,该方案在不明显增加计算量的同时,使分割质量有了明显提高.     

Why DGAs work well on GA-hard functions?

What makes a problem easy or hard for a genetic algorithm (GA)? Much previous work has studied this question by applying Walsh analysis.⁴⁾ In this paper, we demonstrate a function that is GA-hard by analyzing the Walsh coefficients of this function’s Walsh decomposition. Then, we construct five functions with differing degrees of difficulty for genetic algorithms. Some are GA-easy and some are GA-hard. In a previous paper,²⁹⁾ wh have proposed a novel selection method,disruptive selection. This method devotes more trials to both better solutions and worse solutions than it does to moderate solutions, whereas the conventional method allocates its attention according to the performance of each solution. Experimental results show that DGAs (GAs using disruptive selection) perform very well on both GA-easy and GA-hard functions. Finally, we discuss why DGAs outperform conventional GAs.     

遗传神经网络在图像分割中应用研究

针对图像分割的复杂性,利用遗传算法对BP神经网络的权值和阈值进行优化,设计出误差最小的神经网络,然后再对图像的像素进行分类识别,实现并提高了图像分割性能。仿真实验表明,与传统的图像分割方法相比,取得了比传统方法更好的图像分割效果。     

一种基于遗传算法的QoS多播路由算法

深入研究基于遗传算法的QoS多播路由算法,建立支持QoS的多播路由模型.对已有的QoS多播路由算法进行优化,提出适用于下一代网络的基于遗传算法的QoS多播路由算法.采用定长的染色体编码和预处理机制降低算法复杂度.仿真试验表明,该算法收敛速度快,可靠性高,能够更好地满足多播业务的需要.     

基于时空结合概率模型的运动估计和分割方法

探讨了一种基于贝叶斯框架的时空标记场最大后验边缘概率与最大后验概率相结合的运动对象分割算法.通过建立贝叶斯分布模型,求得对象分割标记场的最大后验概率,引入最大后验边缘概率求取最小能量.该算法将时间域分割结果作为初始标记场,空间域的分割结果作为图像的观察场,获取初始运动数目以及相应的运动模型的初始参数,然后通过参数估计,不断更新模型参数,之后通过把每个运动区域和运动模型相关联,来估计运动区域,最终达到分割的目的.实验结果证明,研究的方法对运动目标分割具有较好的分割效果.     

基于Ahn改进遗传算法的路由优化算法的研究

论述了用Ahn改进遗传算法解决路由路径的优化问题,采用可变长度染色体路由串和它的基因节点应用于编码问题,交叉操作在交叉点进行部分染色体部分路由交换,变异操作维持种群的多样性。该算法采用简单维护操作,维护好所有的不可行的染色体。交叉操作和变异操作相结合保证了最优解的搜索能力和解的全局收敛性。计算机仿真实验表明该算法快速有效、可靠性高。     

基于遗传量子的自适应图像分割算法

虚拟娱乐照相领域的图像分割,目的在于实时地提取显示出背景中的感兴趣目标,但由于常用的分割方法容易丢失图像的边缘细节信息,而且运算时间长,难以实现质量与速度的平衡,为此文章提出了一种基于遗传量子进化算法的图像分割方法。该方法首先运用遗传量子方法进行阈值搜索,然后进行区域分割,再利用形态学方法对图像边缘细节进行补偿,并去除噪声。仿真结果表明,与传统方法比较,该方法能有效地提取图像中的人像,保留细节边缘。     

基于遗传算法的最短路径路由优化算法

论述了用启发式遗传算法解决最短路径路由的优化问题,采用可变长度染色体(路由串)和它的基因(节点)应用于编码问题,交叉操作在交叉点进行部分染色体(部分路由)交换,变异操作维持种群的多样性.该算法采用简单维护操作维护好所有的不可行的染色体.交叉操作和变异操作相结合保证了最优解的搜索能力和解的全局收敛性.计算机仿真实验表明该算法快速有效、可靠性高.