自适应天牛须优化与K均值聚类的图像分割算法研究

Research on image segmentation algorithm based on automatic beetle antennae search and K-means clustering

  • 摘要: 在机器识别中,图像分割是重要的一个步骤,传统分割手段存在一定缺陷。针对传统K均值聚类分割的初始聚类中心敏感的缺陷进行了优化,利用自适应天牛须优化算法,避免了这一问题。通过实验结果表明,该算法(ABASK)对图像进行分割,既可以保证图像轮廓的分割,同时还可以更多地保留图像细节。

     

    Abstract: In machine recognition, image segmentation is an important step, and traditional segmentation methods have certain defects. In this paper, the initial cluster center sensitive defects of traditional K-means clustering segmentation are optimized, and the Automatic Beetle Antennae Search (ABASK) is used to avoid this problem. The experimental results show that the ABASK segmentation of the image can not only ensure the segmentation of the image contour, but also preserve the image details.

     

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