图像分割中的标准和遗传k-均值聚类技术

D. Malyszko, S. Wierzchon
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引用次数: 47

摘要

聚类或数据分组是图像处理的关键初始步骤。本文讨论了标准k-均值聚类算法和遗传k-均值聚类算法在图像分割领域中的应用。为了评估和比较两个版本的k-means算法及其变体,已经设计和实现了适当的程序和软件。实验结果表明,遗传优化的k-means算法在图像分析领域证明了其有效性,产生了相当甚至更好的分割结果。
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Standard and Genetic k-means Clustering Techniques in Image Segmentation
Clustering or data grouping is a key initial procedure in image processing. This paper deals with the application of standard and genetic k-means clustering algorithms in the area of image segmentation. In order to assess and compare both versions of k-means algorithm and its variants, appropriate procedures and software have been designed and implemented. Experimental results point that genetically optimized k-means algorithms proved their usefulness in the area of image analysis, yielding comparable and even better segmentation results.
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