Izvorni znanstveni članak

https://doi.org/10.17559/TV-20171125161944

A Linear Fitting Density Peaks Clustering Algorithm for Image Segmentation

You Zhou orcid.org/0000-0003-0013-1281 ; College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, 2699 Qianjin Street, Changchun, 130012, China
Tiantian Zhao ; College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun, 130012, China
Yizhang Wang ; College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun, 130012, China
Jianan Wu ; College of Computer Science and Technology, Changchun University, 6543 Weixing Road, Changchun 130022, China
Xu Zhou orcid.org/0000-0003-0013-1281 ; College of Computer Science and Technology, Jilin University / Center for Computer Fundamental Education, Jilin University, 2699 Qianjin Street, Changchun, 130012, China

Sažetak

Clustering by fast search and finding of density peaks algorithm (DPC) is a recently developed method and can obtain promising results. However, DPC needs users to determine the number of clusters in advance, thus the clustering results are unstable and deeply influenced by the number of clusters. To address this issue, we proposed a novel algorithm, namely LDPC (Linear fitting Density Peaks Clustering algorithm). LDPC uses a novel linear fitting method to choose cluster centres automatically. In the experiments, we use public datasets to access the effectiveness of LDPC. Especially, we applied LDPC to image segmentation tasks. The experimental results show that LDPC can obtain competitive results compared with other clustering algorithms.

Ključne riječi

clustering; density peaks; image segmentation; linear fitting

Hrčak ID:

202625

URI

https://hrcak.srce.hr/202625

Datum izdavanja:

28.6.2018.

Posjeta: 1.816 *