基于遗传算法的图像分割

S. Gorokhovskyi, Andrii Moroz
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引用次数: 1

摘要

图像分割是图像处理和分析过程中至关重要的一步。图像分割是将一幅图像分割成多个图像段的过程。图像分割将图像分成更有代表性和更容易检查的片段。单独的表面或物品可以用作这样的作品。图像分割过程用于定位目标及其边界。遗传算法是一种随机搜索方法,其工作原理来源于遗传规律、自然选择和生物进化。它们最吸引人的特点是能够有效地解决组合搜索的复杂问题,因为对解的并行研究在很大程度上消除了停留在局部最优解上而不是寻找全局最优解的可能性。使用遗传算法的要点是,每个像素使用基于局部和全局已计算分段的距离函数与其他像素分组。几乎每一种图像分割算法都包含用于控制分割结果的参数;该遗传系统可以动态改变参数以达到最佳性能。与图像排序类似,为了优化过程中的多个参数,使用了多目标遗传算法,从而可以找到具有更多变量的多种解决方案集合。多目标遗传算法(MTGA)是一种由优化技术组成的引导随机搜索方法。它可以解决多目标优化问题,并探索解空间的不同部分。因此,可以找到多样化的解决方案集合,同时有更多的变量可以优化。本文对几种MTGA进行了应用和比较。遗传算法是在缺乏高质量标记数据集的情况下进行图像处理的一个很好的工具,这要么是许多研究人员长期工作的结果,要么是大量资金的贡献,以从外部来源获得一系列数据。在本文中,我们将使用遗传算法来解决图像分割问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Segmentation Using Genetic Algorithms
Image segmentation is a crucial step in the image processing and analysis process. Image segmentation is the process of splitting one image into many segments. Image segmentation divides images into segments that are more representative and easier to examine. Individual surfaces or items can be used as such pieces. The process of image segmentation is used to locate objects and their boundaries.Genetic algorithms are stochastic search methods, the work of which is taken from the genetic laws, natural selection, and evolution of organisms. Their main attractive feature is the ability to solve complex problems of combinatorial search effectively, because the parallel study of solutions, largely eliminates the possibility of staying on the local optimal solution rather than finding a global one.The point of using genetic algorithms is that each pixel is grouped with other pixels using a distance function based on both local and global already calculated segments. Almost every image segmentation algorithm contains parameters that are used to control the segmentation results; the genetic system can dynamically change parameters to achieve the best performance.Similarly to image sequencing, to optimize several parameters in the process, multi-targeted genetic algorithms were used, which enabled finding a diverse collection of solutions with more variables. Multi- targeted Genetic Algorithm (MTGA) is a guided random search method that consists of optimization techniques. It can solve multi-targeted optimization problems and explore different parts of the solution space. As a result, a diversified collection of solutions can be found, with more variables that can be optimized at the same time. In this article several MTGA were used and compared.Genetic algorithms are a good tool for image processing in the absence of a high-quality labeled data set, which is either a result of the long work of many researchers or the contribution of large sums of money to obtain an array of data from external sources.In this article, we will use genetic algorithms to solve the problem of image segmentation.
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