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首页> 外文期刊>Electronic Letters on Computer Vision and Image Analysis: ELCVIA >Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering
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Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering

机译：基于并行遗传算法的聚类对象和背景分类

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In this paper, two novel strategies have been proposed to obtain segmentation of an object and background in a given scene. The first one, known as Featureless(FL) approach, deals with the histogram of the original image where Parallel Genetic Algorithm (PGA) based clustering notion is used to determine the optimal threshold from the discrete nature of the histogram distribution. In this regard, we have proposed a new interconnection model for PGA. The second scheme, the Featured Based(FB) approach, is based on the proposed featured histogram distribution. A feature from the given image is extracted and the histogram corresponding to the derived feature pixels is used to determine the optimal threshold for the original image. The proposed PGA based clustering is used to determine the optimal threshold. The performance of both the schemes is compared with that of Otsu's and Kwon's method and FB method is found to be the best among the three techniques. ? keywords: Parallel Genetic Algorithm, Thresholding
机译：在本文中，提出了两种新颖的策略来在给定场景中获得对象和背景的分割。第一种称为Featureless（FL）方法，用于处理原始图像的直方图，其中基于并行遗传算法（PGA）的聚类概念用于根据直方图分布的离散性质确定最佳阈值。在这方面，我们提出了一种新的PGA互连模型。第二种方案是基于特征的（FB）方法，它基于提出的特征直方图分布。从给定图像中提取特征，并使用与导出的特征像素对应的直方图确定原始图像的最佳阈值。所提出的基于PGA的聚类用于确定最佳阈值。将这两种方案的性能与Otsu和Kwon方法的性能进行了比较，发现FB方法是三种技术中最好的。 ？关键词：并行遗传算法阈值

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