Color image segmentation based on multiobjective artificial bee colony optimization

This paper presents a new color image segmentation method based on a multiobjective optimization algorithm, named improved bee colony algorithm for multi-objective optimization (IBMO). Segmentation is posed as a clustering problem through grouping image features in this approach, which combines IBMO with seeded region growing (SRG). Since feature extraction has a crucial role for image segmentation, the presented method is firstly focused on this manner. The main features of an image: color, texture and gradient magnitudes are measured by using the local homogeneity, Gabor filter and color spaces. Then SRG utilizes the extracted feature vector to classify the pixels spatially. It starts running from centroid points called as seeds. IBMO determines the coordinates of the seed points and similarity difference of each region by optimizing a set of cluster validity indices simultaneously in order to improve the quality of segmentation. Finally, segmentation is completed by merging small and similar regions. The proposed method was applied on several natural images obtained from Berkeley segmentation database. The robustness of the proposed ideas was showed by comparison of hand-labeled and experimentally obtained segmentation results. Besides, it has been seen that the obtained segmentation results have better values than the ones obtained from fuzzy c-means which is one of the most popular methods used in image segmentation, non-dominated sorting genetic algorithm II which is a state-of-the-art algorithm, and non-dominated sorted PSO which is an adapted algorithm of PSO for multi-objective optimization.     

An adaptive artificial bee colony algorithm based on objective function value information

Artificial bee colony (ABC) algorithm is a novel biological-inspired optimization algorithm, which has many advantages compared with other optimization algorithm, such as less control parameters, great global optimization ability and easy to carry out. It has proven to be more effective than some evolutionary algorithms (EAs), particle swarm optimization (PSO) and differential evolution (DE) when testing on both benchmark functions and real issues. ABC, however, its solution search equation is poor at exploitation. For overcoming this insufficiency, two new solution search equations are proposed in this paper. They apply random solutions to take the place of the current solution as base vector in order to get more useful information. Exploitation is further improved on the basis of enhancing exploration by utilizing the information of the current best solution. In addition, the information of objective function value is introduced, which makes it possible to adjust the step-size adaptively. Owing to their respective characteristics, the new solution search equations are combined to construct an adaptive algorithm called MTABC. The methods our proposed balance the exploration and exploitation of ABC without forcing severe extra overhead in respect of function evaluations. The performance of the MTABC algorithm is extensively judged on a set of 20 basic functions and a set of 10 shifted or rotated functions, and is compared favorably with other improved ABCs and several state-of-the-art algorithms. The experimental results show that the proposed algorithm has a higher convergence speed and better search ability for almost all functions.     

基于DWT-SVD域的彩色图像自适应水印算法

为了保护数字彩色图像版权,提出了一种结合离散小波变换(DWT)和矩阵奇异值分解(SVD)的彩色图像自适应水印算法。其主要设计思想是:先将原RGB彩色图像的各颜色分量进行小波分解;再将得到的中、低频带的小波系数与原水印图像分别进行奇异值分解,水印信息的奇异值重复嵌入到三通道中、低频带的小波系数奇异值中,且利用临界视觉阈值与奇异值之间的关系对水印嵌入强度作自适应调节,从而达到增强水印鲁棒性和确保水印透明性的目的。本算法适用于二值及灰度水印,在二值水印的提取过程中需设定一个提取阈值,以保证提取水印的完整性。仿真结果说明本算法对水印系统的透明性与鲁棒性作了较好的协调,是一种较稳健的算法。     

An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem

The increasing fuel price has led to high operational cost and therefore, advanced optimal dispatch schemes need to be developed to reduce the operational cost while maintaining the stability of grid. This study applies an improved heuristic approach, the improved Artificial Bee Colony (IABC) to optimal power flow (OPF) problem in electric power grids. Although original ABC has provided robust solutions for a range of problems, such as the university timetabling, training neural networks and optimal distributed generation allocation, its poor exploitation often causes solutions to be trapped in local minima. Therefore, in order to adjust the exploitation and exploration of ABC, the IABC based on the orthogonal learning is proposed. Orthogonal learning is a strategy to predict the best combination of two solution vectors based on limited trials instead of exhaustive trials, and to conduct deep search in the solution space. To assess the proposed method, two fuel cost objective functions with high non-linearity and non-convexity are selected for the OPF problem. The proposed IABC is verified by IEEE-30 and 118 bus test systems. In all case studies, the IABC has shown to consistently achieve a lower cost with smaller deviation over multiple runs than other modern heuristic optimization techniques. For example, the quadratic fuel cost with valve effect found by IABC for 30 bus system is 919.567 $/hour, saving 4.2% of original cost, with 0.666 standard deviation. Therefore, IABC can efficiently generate high quality solutions to nonlinear, nonconvex and mixed integer problems.     

基于混沌和HVS的小波域自适应图像水印算法

针对常规的小波域水印算法自适应性差的缺陷,提出了一种新的基于混沌和人类视觉系统的小波域自适应图像水印算法。首先对载体图像进行二级DWT变换;然后对特定的中频子带进行分块,并对每块做SVD分解;最后将Logistic混沌置乱后的二值水印信息嵌入到每块的最大奇异值中,嵌入的强度根据HVS原理自适应地进行调整。仿真实验表明,该算法不仅具有很好的不可见性,且对JPEG压缩、加噪、滤波等常见的图像攻击具有很强的鲁棒性。     

一种求解高维约束优化问题的人工蜂群算法*

针对求解高维约束优化中算法的收敛速度和解的精度不高的缺点,提出一种改进的人工蜂群约束优化算法。该算法在初始化种群和侦察蜂探寻新蜜源时采用了正交实验设计方法,并在采蜜蜂搜索时使用了改进的高斯分布估计,跟随蜂按照采蜜蜂的适应值大小选择一个采蜜蜂,在其蜜源领域内采用差异算法搜索新的蜜源;在处理约束条件时采用自适应优劣解比较方法。最后通过13个标准的Benchmark测试函数进行仿真实验,结果表明该算法在处理高维约束优化问题时具有较好的收敛性和稳定性。     

基于人工蜂群与模糊C均值的自适应小波变换的噪声图像分割

针对传统模糊C均值(FCM)聚类算法在处理噪声图像时易受到噪声影响的问题,提出了基于FCM的小波域特征增强的噪声图像分割方法.首先,将噪声图像进行二维小波分解;其次,对近似系数进行边缘增强,同时利用人工蜂群(ABC)优化算法对细节系数进行阈值处理,并将处理后的系数进行小波重构;最后,对重构后的图片使用FCM算法来进行图...     

图像块的自适应均衡水印算法

目的 针对水印算法通常利用实验确定强度参数,实验工作量大并且具有随机性,得到的参数无法较好地均衡水印不可见性和鲁棒性,提出一种基于图像块的自适应均衡水印算法。方法 利用尺度不变特征变换（SIFT）提取原始图像中鲁棒性强的特征点作为水印嵌入区域,将提取的嵌入区域分成4个大小相等且互不重叠的图像块,并对各图像块进行奇异值分解（SVD）,得到与各块相应的奇异值矩阵,各块与水印做一级离散小波变换后产生的各子带相叠加,生成嵌入加密水印块,重组得到水印矩阵,降维后将特征点还原到原始图像。根据果蝇优化算法（FOA）中适应度函数迭代确定加密水印强度参数,构造水印图像的自适应嵌入,来均衡水印的不可见性和鲁棒性,水印检测可直接作用在受攻击后的图像上,无需校正恢复。结果 对标准灰度图像进行多组实验,得到含水印图像峰值信噪比均达到43dB以上;对水印载体图像分别进行噪声、压缩、剪切、旋转仿真攻击实验,提取水印图像与原始水印图像的归一化相关系数都达到0.94以上。结论 SIFT算法实现图像块局部嵌入,提取特征点稳定性强,结合SVD算法使水印嵌入性能良好,利用FOA算法自适应确定最优参数,使水印图像嵌入效果达到最佳状态,最终均衡了水印的不可见性和鲁棒性。     

基于3D-DCT和SVD的鲁棒彩色图像水印算法

在数字图像水印领域,水印算法主要集中于灰度图像,且提出的大部分彩色图像水印算法往往仅在亮度分量或在彩色图像的每一通道中嵌入水印,未能充分利用彩色图像的冗余空间,影响了水印的透明性和鲁棒性。针对此问题,提出了一种新颖的基于三维离散余弦变换和奇异值分解的彩色图像水印算法。算法先对水印图像进行预处理和对彩色图像进行互不重叠的分块;其次对每一分块进行三维离散余弦变换;最后选择对三维离散余弦变换系数的第一分量进行奇异值分解。嵌入水印时,对三维离散余弦变换系数第一分量的最大奇异值和第二分量分别采用量化和关系的嵌入方法嵌入水印。提取水印时,分别采用量化和关系提取算法提取水印并进行比较,选取相似值高的水印图像作为最终提取的水印。实验结果表明,提出的算法具有较好透明性的同时,具有抵抗常规信号处理和模糊、扭曲及锐化等攻击的能力。     

一种基于混沌置乱和SVD的小波域盲水印算法

提出了一种基于混沌和奇异值分解相结合的数字水印算法.该算法对原始图像做离散小波变换后得到4个子带,并将低频子带(LL1)分成互不重叠的8×8分块,然后对每一块做奇异值分解,将混沌置乱后的水印信息嵌入每块中最大的奇异值中.实验结果表明,该算法具有较好的不可见性、鲁棒性和安全性.     

Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM

The generation of leak along the pipeline carrying crude oils and liquid fuels results enormous financial loss to the industry and also affects the public health. Hence, the leak detection and localization problem has always been a major concern for the companies. In spite of the various techniques developed, accuracy and time involved in the prediction is still a matter of concern. In this paper, a novel leak detection scheme based on rough set theory and support vector machine (SVM) is proposed to overcome the problem of false leak detection. In this approach, ‘rough set theory’ is explored to reduce the length of experimental data as well as generate rules. It is embedded to enhance the decision making process. Further, SVM classifier is employed to inspect the cases that could not be detected by applied rules. For the computational training of SVM, this paper uses swarm intelligence technique: artificial bee colony (ABC) algorithm, which imitates intelligent food searching behavior of honey bees. The results of proposed leak detection scheme with ABC are compared with those obtained by using particle swarm optimization (PSO) and one of its variants, so-called enhanced particle swarm optimization (EPSO). The experimental results advocate the use of propounded method for detecting leaks with maximum accuracy.     

基于奇异值分解的彩色图像水印算法

数字水印是将身份确认信息或保密信息镶嵌于图像中的一种技术,可靠的水印能为信息的安全提供可靠的保证.提出了一种基于奇异值分解的彩色图像水印算法.首先对彩色图像的RGB色彩空间的绿色分量无重叠地分成8×8块,对各个子块进行奇异值分解,最后将二值图像混沌置乱加密后随机嵌入到各个子块的奇异值中.实验结果表明,该算法不仅具有较好的透明性,而且对常规的图像处理具有较好的稳健性.     

A new robust and secure digital image watermarking scheme based on the integer wavelet transform and singular value decomposition

In this paper, a new robust and secure digital image watermarking scheme that can be used for copyright protection is proposed. The scheme uses the integer wavelet transform (IWT) and singular value decomposition (SVD). The grey image watermark pixels values are embedded directly into the singular values of the 1-level IWT decomposed sub-bands. Experimental results demonstrate the effectiveness of the proposed scheme in terms of robustness, imperceptibility and capacity due to the IWT and SVD properties. A challenge due to the false positive problem which may be faced by most of SVD-based watermarking schemes has been solved in this work by adopting a digital signature into the watermarked image. The proposed digital signature mechanism is applied to generate and embed a digital signature after embedding the watermarks; the ownership is then authenticated before extracting watermarks. Thus, the proposed scheme achieved the security issue where the false positive problem is solved, in addition to that, the scheme is considered as a blind scheme. A computer simulation is used to verify the feasibility of the proposed scheme and its robustness against various types of attacks and to compare it with some previous schemes. Furthermore, the statistical Wilcoxon signed rank test is employed to certify the effectiveness of the proposed scheme.     

图像块的不可见性与鲁棒性均衡水印算法

目的 为协调水印算法不可见性与鲁棒性之间的矛盾,提高水印算法抵抗几何攻击的能力,提出一种图像块的不可见性与鲁棒性均衡水印算法。方法 将宿主图像分成互不重叠的图像块,利用人类视觉系统的掩蔽特性对每个图像块的纹理特征和边缘特征进行分析,选择掩蔽性好的图像块作为嵌入子块。对嵌入子块作2级离散小波变换,将其低频子带进行奇异值分解,通过修改U矩阵第1列元素间的大小关系嵌入Arnold置乱后的水印信息。在水印提取前,对几何失真含水印图像利用图像尺度不变特征变换（SIFT）特征点的坐标关系和尺度特征进行几何校正,恢复水印的同步性。结果 对标准灰度图像进行实验,含水印图像的峰值信噪比都可以达到44 dB以上。对含水印图像进行常规攻击和几何攻击,提取出的水印图像与原始水印图像的归一化互相关系数大部分都能达到0.99以上,说明该算法不仅具有良好的不可见性,对常见攻击和几何攻击都具有较强的鲁棒性。结论 选择掩蔽性好的图像块作为水印嵌入位置能够充分保证水印算法的不可见性,特别是水印提取前利用SIFT特征点具有旋转、缩放和平移不变性对几何失真含水印图像实现有效校正,提高了含水印图像抵抗几何攻击的能力,较好地协调水印算法不可见性与鲁棒性之间的矛盾。