Rolling Guidance Filter as a Clustering Algorithm

• HATTORI Takayuki

  Kyushu University

• INOUE Kohei

  Kyushu University

• HARA Kenji

  Kyushu University

Abstract

We propose a generalization of the rolling guidance filter (RGF) to a similarity-based clustering (SBC) algorithm which can handle general vector data. The proposed RGF-based SBC algorithm makes the similarities between data clearer than the original similarity values computed from the original data. On the basis of the similarity values, we assign cluster labels to data by an SBC algorithm. Experimental results show that the proposed algorithm achieves better clustering result than the result by the naive application of the SBC algorithm to the original similarity values. Additionally, we study the convergence of a unimodal vector dataset to its mean vector.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E104.D (10), 1576-1579, 2021-10-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• edge preserving smoothing
• rolling guidance filter
• clustering

Details

• CRID: 1390852514692928256
• NII Article ID: 130008095614
• NII Book ID: AA11510321
• DOI: 10.1587/transinf.2021pcl0001
• ISSN: 17451361
• ISSN: 09168532
• HANDLE: 2324/4783559
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E104.D/10/E104.D_2021PCL0001/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed