Cargando…
Feature extraction of 3D Chinese rose model based on color and shape features
Flower classification is of great importance to the research fields of plants, food, and medicine. Due to more abundant information on three-dimensional (3D) flower models than two-dimensional 2D images, it makes the 3D models more suitable for flower classification tasks. In this study, a feature e...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9745194/ https://www.ncbi.nlm.nih.gov/pubmed/36523632 http://dx.doi.org/10.3389/fpls.2022.1042016 |
_version_ | 1784849095643365376 |
---|---|
author | Liu, Jin’fei Mei, Shu’li Song, Tao Liu, Hong’hao |
author_facet | Liu, Jin’fei Mei, Shu’li Song, Tao Liu, Hong’hao |
author_sort | Liu, Jin’fei |
collection | PubMed |
description | Flower classification is of great importance to the research fields of plants, food, and medicine. Due to more abundant information on three-dimensional (3D) flower models than two-dimensional 2D images, it makes the 3D models more suitable for flower classification tasks. In this study, a feature extraction and classification method were proposed based on the 3D models of Chinese roses. Firstly, the shape distribution method was used to extract the sharpness and contour features of 3D flower models, and the color features were obtained from the Red-Green-Blue (RGB) color space. Then, the RF-OOB method was employed to rank the extracted flower features. A shape descriptor based on the unique attributes of Chinese roses was constructed, χ(2) distance was adopted to measure the similarity between different Chinese roses. Experimental results show that the proposed method was effective for the retrieval and classification tasks of Chinese roses, and the average classification accuracy was approximately 87%, which can meet the basic retrieval requirements of 3D flower models. The proposed method promotes the classification of Chinese roses from 2D space to 3D space, which broadens the research method of flower classification. |
format | Online Article Text |
id | pubmed-9745194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97451942022-12-14 Feature extraction of 3D Chinese rose model based on color and shape features Liu, Jin’fei Mei, Shu’li Song, Tao Liu, Hong’hao Front Plant Sci Plant Science Flower classification is of great importance to the research fields of plants, food, and medicine. Due to more abundant information on three-dimensional (3D) flower models than two-dimensional 2D images, it makes the 3D models more suitable for flower classification tasks. In this study, a feature extraction and classification method were proposed based on the 3D models of Chinese roses. Firstly, the shape distribution method was used to extract the sharpness and contour features of 3D flower models, and the color features were obtained from the Red-Green-Blue (RGB) color space. Then, the RF-OOB method was employed to rank the extracted flower features. A shape descriptor based on the unique attributes of Chinese roses was constructed, χ(2) distance was adopted to measure the similarity between different Chinese roses. Experimental results show that the proposed method was effective for the retrieval and classification tasks of Chinese roses, and the average classification accuracy was approximately 87%, which can meet the basic retrieval requirements of 3D flower models. The proposed method promotes the classification of Chinese roses from 2D space to 3D space, which broadens the research method of flower classification. Frontiers Media S.A. 2022-11-29 /pmc/articles/PMC9745194/ /pubmed/36523632 http://dx.doi.org/10.3389/fpls.2022.1042016 Text en Copyright © 2022 Liu, Mei, Song and Liu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Liu, Jin’fei Mei, Shu’li Song, Tao Liu, Hong’hao Feature extraction of 3D Chinese rose model based on color and shape features |
title | Feature extraction of 3D Chinese rose model based on color and shape features |
title_full | Feature extraction of 3D Chinese rose model based on color and shape features |
title_fullStr | Feature extraction of 3D Chinese rose model based on color and shape features |
title_full_unstemmed | Feature extraction of 3D Chinese rose model based on color and shape features |
title_short | Feature extraction of 3D Chinese rose model based on color and shape features |
title_sort | feature extraction of 3d chinese rose model based on color and shape features |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9745194/ https://www.ncbi.nlm.nih.gov/pubmed/36523632 http://dx.doi.org/10.3389/fpls.2022.1042016 |
work_keys_str_mv | AT liujinfei featureextractionof3dchineserosemodelbasedoncolorandshapefeatures AT meishuli featureextractionof3dchineserosemodelbasedoncolorandshapefeatures AT songtao featureextractionof3dchineserosemodelbasedoncolorandshapefeatures AT liuhonghao featureextractionof3dchineserosemodelbasedoncolorandshapefeatures |