Evaluation of modified adaptive k-means segmentation algorithm
Evaluation of modified adaptive k-means segmentation algorithm
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DOI：
10.1007/s41095-019-0151-2
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发表时间：
2019-12-01
影响因子：
6.9
通讯作者：
Yohannes, Dereje
中科院分区：
文献类型：
--
作者：
Debelee, Taye Girma;Schwenker, Friedhelm;Yohannes, Dereje
分割是通过在区域之间创建边界来将图像划分为不同区域的行为。k-means图像分割是最简单的流行方法。然而，分割质量取决于初始参数（聚类中心及其数量）。在本文中，提出了一种基于卷积的改进自适应k均值（MAKM）方法，并使用从不同来源（MATLAB，伯克利图像数据库，VOC 2012，BGH，MIAS和MRI）收集的图像进行评估。评价结果表明，该算法在图像分割质量（Q值）、计算代价和RMSE方面上级k-means++、模糊c-均值、基于直方图的k-means和减法k-means算法。该算法还在IoU和MIoU方面与最先进的基于学习的方法进行了比较;它实现了更高的MIoU值。
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.