粒子群优化及其在图像分割.doc
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粒子群优化及其在图像分割,摘要粒子群优化算法源于鸟群群体运动行为的研究，是一种基于种群搜索策略的自适应随机优化算法。作为群智能的典型代表，粒子群优化算法已经被证明是一种有效的全局优化方法，一经提出就受到全世界研究者的关注、重视，目前已经被广泛应用于图像分割、目标函数优化、神经网络训练、模糊控制系统等许多领域，并取得了良好的效果。图像分割是目标检...
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摘 要
粒子群优化算法源于鸟群群体运动行为的研究，是一种基于种群搜索策略的自适应随机优化算法。作为群智能的典型代表，粒子群优化算法已经被证明是一种有效的全局优化方法，一经提出就受到全世界研究者的关注、重视，目前已经被广泛应用于图像分割、目标函数优化、神经网络训练、模糊控制系统等许多领域，并取得了良好的效果。
图像分割是目标检测和识别过程中的重要步骤，其目的是将感兴趣的区域从图像中分割出来，从而为计算机视觉的后续处理提供依据。图像分割的方法有多种，阈值法因其实现简单而成为一种有效的图像分割方法。然而要在直方图呈多峰分布的复杂图像中搜索一个最佳多阈值组合对图像进行分割，它的高耗时性无法满足实时性的要求，而阈值的准确确定又是有效分割图像的关键。因此，快速准确地搜索到图像分割的多阈值组合将是问题的难点。要快速和准确地确定复杂图像中的最佳多阈值组合，使分割效果好且满足实时性的要求，就必须寻求一种高效的算法来解决基于多阈值法的图像分割问题。
本文在前人工作的基础上，对粒子群优化算法及其在图像分割中的应用进行了研究：
（1）为了提高粒子群算法的收敛速度并同时提高算法的全局搜索性能，本文着重研究了两种新颖的改进型粒子群算法。（a）第一种改进算法采用相对基初始化粒子种群以获得更优的初始解。该算法为了进一步提高收敛速度及精度，当群体陷入局部最优时，产生相应的变异粒子，比较其适应度，选取适应度高的粒子继续优化进程。通过对不同测试函数的仿真实验表明，该算法显著地提高了粒子群算法的收敛速度和精度。（b）第二种改进算法是将粒子群算法与免疫算法相结合，采用模拟退火机制对粒子的位置进行限制，并用旅行商问题验证了算法在组合优化中的有效性。
（2）将本文改进的两种算法应用于基于多阈值法的图像分割试验中，实验表明：该两种改进算法能快速准确地找到分割阈值的最佳组合，取得好的分割效果且适合多峰直方图的复杂图像。
关键词 粒子群优化算法；图像分割；变异模型；人工免疫；多阈值
Abstract
Particle swarm optimization algorithm (PSO) is inspired by social behavior of bird flocking or fish schooling.It is a population-based, self-adaptive search optimization technique.As a kind of swarm intelligence, it has been proven to be an effective global optimization method. PSO algorithm has attracted a lot of attention from researchers around the world since it was put forward. It has already been successfully used in many areas, such as image segmentation, function optimization, artificial neural network training, and fuzzy system control.
Image segmentation is regarded as an important step in object examination and recognition.The main goal is to separate objects of interest from an acquired image,so it provides the evidence to the subsequent processing of computer vision.Several methods are proposed from different theoretical point of view for image segmentation. Image threshold segmentation is an effective tool for image segmentation because the simple implemention. However, the problem of time-consuming computation will not meet real-time requirement when we try to search optimum multilevel thresholds on a multimodal histogram of a complex image. But exactly determining those thresholds is the key for effective image segmentation.So it is a difficult problem for us to quickly and exactly search optimum multilevel thresholds for image segmentation.However, to quickly and exactly determine optimum combination of multilevel thresholds, which can segment the image efficiently and meet real-time requirement, we must explore an effective and rapid algorithm to solve the problem of image segmentation based on multilevel thresholds.
Based on the former research,the author studies the improvement of particle swarm algorithm and its application on image segmentation：
Firstly, in order to improve the particle swarm algorithm convergence speed and also improve the global search function algorithm, this paper focuses on rearching two novel improved particle swarm algorithm. (a)The first kind of improved algorithm is adopted Opposition-based Learning initialization particle population, to gain more optimal initial solution. This Algorithm in order to further enhances the convergence speed and precision, when the group into the local optimal, produced the corresponding variation particles, compare their fitness, the selection of the best fitness particle continue to optimize process. According to the different test function of simulation experiment shows that the improved particle swarm algorithm is significantly improved tne algorithm convergence speed and precision. (b)The second kind of improved algorithm combins particle swarm algorithm with immune algorithm and using simulated annealing mechanism of particle position limit, and traveling salesman problem verifies the effectiveness of the combinatorial optimization algorithm.
Secondly, the two improved algorithms are applied to image segmentation experiments, based on multi threshold value. The experiment showed that the two improved algorithms can rapidly and accurately find the best combination of thresholds, obtain good segmentation results and suitable for complex image with multi-modal histogram.
Key words Particle swarm optimization algor..
粒子群优化算法源于鸟群群体运动行为的研究，是一种基于种群搜索策略的自适应随机优化算法。作为群智能的典型代表，粒子群优化算法已经被证明是一种有效的全局优化方法，一经提出就受到全世界研究者的关注、重视，目前已经被广泛应用于图像分割、目标函数优化、神经网络训练、模糊控制系统等许多领域，并取得了良好的效果。
图像分割是目标检测和识别过程中的重要步骤，其目的是将感兴趣的区域从图像中分割出来，从而为计算机视觉的后续处理提供依据。图像分割的方法有多种，阈值法因其实现简单而成为一种有效的图像分割方法。然而要在直方图呈多峰分布的复杂图像中搜索一个最佳多阈值组合对图像进行分割，它的高耗时性无法满足实时性的要求，而阈值的准确确定又是有效分割图像的关键。因此，快速准确地搜索到图像分割的多阈值组合将是问题的难点。要快速和准确地确定复杂图像中的最佳多阈值组合，使分割效果好且满足实时性的要求，就必须寻求一种高效的算法来解决基于多阈值法的图像分割问题。
本文在前人工作的基础上，对粒子群优化算法及其在图像分割中的应用进行了研究：
（1）为了提高粒子群算法的收敛速度并同时提高算法的全局搜索性能，本文着重研究了两种新颖的改进型粒子群算法。（a）第一种改进算法采用相对基初始化粒子种群以获得更优的初始解。该算法为了进一步提高收敛速度及精度，当群体陷入局部最优时，产生相应的变异粒子，比较其适应度，选取适应度高的粒子继续优化进程。通过对不同测试函数的仿真实验表明，该算法显著地提高了粒子群算法的收敛速度和精度。（b）第二种改进算法是将粒子群算法与免疫算法相结合，采用模拟退火机制对粒子的位置进行限制，并用旅行商问题验证了算法在组合优化中的有效性。
（2）将本文改进的两种算法应用于基于多阈值法的图像分割试验中，实验表明：该两种改进算法能快速准确地找到分割阈值的最佳组合，取得好的分割效果且适合多峰直方图的复杂图像。
关键词 粒子群优化算法；图像分割；变异模型；人工免疫；多阈值
Abstract
Particle swarm optimization algorithm (PSO) is inspired by social behavior of bird flocking or fish schooling.It is a population-based, self-adaptive search optimization technique.As a kind of swarm intelligence, it has been proven to be an effective global optimization method. PSO algorithm has attracted a lot of attention from researchers around the world since it was put forward. It has already been successfully used in many areas, such as image segmentation, function optimization, artificial neural network training, and fuzzy system control.
Image segmentation is regarded as an important step in object examination and recognition.The main goal is to separate objects of interest from an acquired image,so it provides the evidence to the subsequent processing of computer vision.Several methods are proposed from different theoretical point of view for image segmentation. Image threshold segmentation is an effective tool for image segmentation because the simple implemention. However, the problem of time-consuming computation will not meet real-time requirement when we try to search optimum multilevel thresholds on a multimodal histogram of a complex image. But exactly determining those thresholds is the key for effective image segmentation.So it is a difficult problem for us to quickly and exactly search optimum multilevel thresholds for image segmentation.However, to quickly and exactly determine optimum combination of multilevel thresholds, which can segment the image efficiently and meet real-time requirement, we must explore an effective and rapid algorithm to solve the problem of image segmentation based on multilevel thresholds.
Based on the former research,the author studies the improvement of particle swarm algorithm and its application on image segmentation：
Firstly, in order to improve the particle swarm algorithm convergence speed and also improve the global search function algorithm, this paper focuses on rearching two novel improved particle swarm algorithm. (a)The first kind of improved algorithm is adopted Opposition-based Learning initialization particle population, to gain more optimal initial solution. This Algorithm in order to further enhances the convergence speed and precision, when the group into the local optimal, produced the corresponding variation particles, compare their fitness, the selection of the best fitness particle continue to optimize process. According to the different test function of simulation experiment shows that the improved particle swarm algorithm is significantly improved tne algorithm convergence speed and precision. (b)The second kind of improved algorithm combins particle swarm algorithm with immune algorithm and using simulated annealing mechanism of particle position limit, and traveling salesman problem verifies the effectiveness of the combinatorial optimization algorithm.
Secondly, the two improved algorithms are applied to image segmentation experiments, based on multi threshold value. The experiment showed that the two improved algorithms can rapidly and accurately find the best combination of thresholds, obtain good segmentation results and suitable for complex image with multi-modal histogram.
Key words Particle swarm optimization algor..
