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基于改进K-means算法的图像分割

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图像分割在图像分析的整个流程中占据关键地位,是图像理解中的重要步骤,同时,它也被看作是图像处理领域最有挑战性的难题之一.因此该研究提出一个基于改进K-means算法的图像分割方法.对图片进行等切选取初始簇心,设定阈值合并多余的簇,给定平均直径优化簇心数量及分类效果.通过实验,验证了该方法的有效性.
Image segmentation based on K-means algorithm
Image segmentation plays a crucial role in the entire process of image analysis and is an important step in image understanding.At the same time,it is also considered one of the most challenging challenges in the field of image processing.Therefore,this study proposes an image segmentation method based on the improved K-means algorithm.Perform equicutting on the image to select the initial cluster center,set a threshold to merge excess clusters,and optimize the number of cluster centers and classification performance with an average diameter.The effectiveness of this method has been verified through experiments.

K-means algorithmimage segmentationequicuttingaverage diameter

李恒博、刘静超、吴珂彤

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西京学院计算机学院,西安 710000

K-means算法 图像分割 等切 平均直径

陕西省大学生创新训练项目

S202312715034

2024

现代计算机
中大控股

现代计算机

影响因子:0.292
ISSN:1007-1423
年,卷(期):2024.30(2)
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