| Item type |
デフォルトアイテムタイプ（シンプル）(1) |
| 公開日 |
2025-10-24 |
| タイトル |
|
|
タイトル |
Making clustering methods workable for shapes using the ordinary Procrustes sum of squares |
|
言語 |
en |
| 作成者 |
岩田, 一貴
呑谷, 祐輝
三村, 和史
|
| 権利情報 |
|
|
権利情報 |
© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Ordinary Procrustes sum of squares |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
K-means clustering |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Fuzzy c-means clustering |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Spectral clustering |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Mean shift clustering |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Convex clustering |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
In the last few decades, a number of clustering methods for vectorial data have matured. Occasionally, researchers are interested in making them workable for data of other types, such as an object’s shape. The current difficulty in applying them to shape clustering is that clustering must be invariant to several similarity transformations of shapes. Therefore, we use the ordinary Procrustes sum of squares (OSS) in Procrustes analysis, which refers to analysis that is invariant to the similarity transformations between shapes. Thus, in this study, we aim to make mature methods workable in shape clustering. To achieve this, the essential points that we need to address are to rewrite the optimization problem associated with each method and to present a feasible method for computing the solution to the problem. As a result, we base the method on the OSS, its derivative, or a symmetric matrix concerning the OSS. Another aim of this study is to identify specific applications of shape clustering. We demonstrate the applications through experiments using datasets that contain skewed line drawings, American football formations, and baseball pitch trajectories. We examine the OSS-based methods by considering shape classification and determining the best method for implementing the OSS. As a result, regardless of the dataset, we demonstrate that the OSS in every method is more effective than other shape distances and that the convex clustering method incorporating the OSS is best performed in several ways. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Elsevier B.V. |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子（シンプル） |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ（シンプル） |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 関連情報 |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1016/j.patcog.2025.111878 |
| 書誌情報 |
en : Pattern Recognition
巻 169,
p. 111878-none,
ページ数 10,
発行日 2025-06-16
|
PR169_11878.pdf (2.0 MB)
