Rolling guidance filter as a clustering algorithm

Takayuki Hattori, Kohei Inoue, Kenji Hara

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1576-1579
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE104D
Issue number10
DOIs
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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