基于聚类集成的图像分割算法

Lei Wang, Guoyin Zhang
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引用次数: 5

摘要

通过对图像的分割进行整合,形成最终的图像聚类是一种有效的图像处理方法,称为图像聚类集成。与传统的单聚类算法相比,该算法提高了图像处理的精度和稳定性。本文利用K-Means和Nyström谱聚类(CEKMNSC)设计了一种新的图像分割算法——聚类集成算法。该算法计算复杂度低。它采用集群集成方案。在聚类中,该算法使用k-means算法创建一组分割结果。在集成处理中,算法基于Nyström方法对分区结果进行集成。实验结果表明,CEKMNSC算法具有较高的聚类质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cluster Ensemble Based Image Segmentation Algorithm
By integrating image segmentations to form a final image cluster is an effective image process, called image cluster ensemble. The image processing improves the accuracy and stability from traditional single clustering based algorithm. In the paper, we design a novel image partition algorithm, called Cluster Ensemble algorithm by using the K-Means and Nyström Spectral Clustering (CEKMNSC). The algorithm requires low computational complexity. It adopts a cluster ensemble scheme. In clustering, the algorithm uses a k-means algorithm to create a set of segmentations results. In ensemble processing, the algorithm integrates partition results based on the Nyström method. Our experimental results show that CEKMNSC algorithm has higher quality of clustering.
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