Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. However, the segmentation quality is contingent on the initial parameters (the cluster centers and their number). In this paper, a convolution-based modified adaptive k-means (MAKM) approach is proposed and evaluated using images collected from different sources (MATLAB, Berkeley image database, VOC2012, BGH, MIAS, and MRI). The evaluation shows that the proposed algorithm is superior to k-means++, fuzzy c-means, histogram-based k-means, and subtractive k-means algorithms in terms of image segmentation quality (Q-value), computational cost, and RMSE. The proposed algorithm was also compared to state-of-the-art learning-based methods in terms of IoU and MIoU; it achieved a higher MIoU value.

中文翻译：

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改进的自适应k均值分割算法的评估

分割是通过在区域之间创建边界将图像划分为不同区域的行为。k均值图像分割是最简单的流行方法。但是，分割质量取决于初始参数（聚类中心及其数量）。本文提出了一种基于卷积的改进的自适应k均值（MAKM）方法，并使用了从不同来源（MATLAB，伯克利图像数据库，VOC2012，BGH，MIAS和MRI）收集的图像进行评估。评估表明，该算法优于k -mean ++，模糊c -means，基于直方图的k -means和减法k-在图像分割质量（Q值），计算成本和RMSE方面表示算法。就IoU和MIoU而言，该算法也与基于学习的最新方法进行了比较。它实现了更高的MIoU值。