A Linear Fitting Density Peaks Clustering Algorithm for Image Segmentation

• Main Authors: Tiantian Zhao, Yizhang Wang, Jianan Wu, Xu Zhou, You Zhou
• Format: Article
• Language: English
• Published: Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2018-01-01
• Series: Tehnički Vjesnik
• Subjects: clustering; density peaks; image segmentation; linear fitting
• Online Access: https://hrcak.srce.hr/file/298277
• ISSN: 1330-3651; 1848-6339

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.