IEICE TRANSACTIONS on Information
Rolling Guidance Filter as a Clustering Algorithm
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.10 pp.1576-1579
- Publication Date
- 2021/10/01
- Publicized
- 2021/05/31
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021PCL0001
- Type of Manuscript
- Special Section LETTER (Special Section on Picture Coding and Image Media Processing)
- Category
Authors
Takayuki HATTORI
Kyushu University
Kohei INOUE
Kyushu University
Kenji HARA
Kyushu University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Takayuki HATTORI, Kohei INOUE, Kenji HARA, "Rolling Guidance Filter as a Clustering Algorithm" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 10, pp. 1576-1579, October 2021, doi: 10.1587/transinf.2021PCL0001.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021PCL0001/_p
Copy
@ARTICLE{e104-d_10_1576,
author={Takayuki HATTORI, Kohei INOUE, Kenji HARA, },
journal={IEICE TRANSACTIONS on Information},
title={Rolling Guidance Filter as a Clustering Algorithm},
year={2021},
volume={E104-D},
number={10},
pages={1576-1579},
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.},
keywords={},
doi={10.1587/transinf.2021PCL0001},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Rolling Guidance Filter as a Clustering Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 1576
EP - 1579
AU - Takayuki HATTORI
AU - Kohei INOUE
AU - Kenji HARA
PY - 2021
DO - 10.1587/transinf.2021PCL0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2021
AB - 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.
ER -
English
