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Superpixel Segmentation Based on Grid Point Density Peak Clustering

Superpixel segmentation is one of the key image preprocessing steps in object recognition and detection methods. However, the over-segmentation in the smoothly connected homogenous region in an image is the key problem. That would produce redundant complex jagged textures. In this paper, the density...

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Autores principales: Chen, Xianyi, Peng, Xiafu, Wang, Sun’an
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512046/
https://www.ncbi.nlm.nih.gov/pubmed/34640692
http://dx.doi.org/10.3390/s21196374
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author Chen, Xianyi
Peng, Xiafu
Wang, Sun’an
author_facet Chen, Xianyi
Peng, Xiafu
Wang, Sun’an
author_sort Chen, Xianyi
collection PubMed
description Superpixel segmentation is one of the key image preprocessing steps in object recognition and detection methods. However, the over-segmentation in the smoothly connected homogenous region in an image is the key problem. That would produce redundant complex jagged textures. In this paper, the density peak clustering will be used to reduce the redundant superpixels and highlight the primary textures and contours of the salient objects. Firstly, the grid pixels are extracted as feature points, and the density of each feature point will be defined. Secondly, the cluster centers are extracted with the density peaks. Finally, all the feature points will be clustered by the density peaks. The pixel blocks, which are obtained by the above steps, are superpixels. The method is carried out in the BSDS500 dataset, and the experimental results show that the Boundary Recall (BR) and Achievement Segmentation Accuracy (ASA) are 95.0% and 96.3%, respectively. In addition, the proposed method has better performance in efficiency (30 fps). The comparison experiments show that not only do the superpixel boundaries have good adhesion to the primary textures and contours of the salient objects, but they can also effectively reduce the redundant superpixels in the homogeneous region.
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spelling pubmed-85120462021-10-14 Superpixel Segmentation Based on Grid Point Density Peak Clustering Chen, Xianyi Peng, Xiafu Wang, Sun’an Sensors (Basel) Article Superpixel segmentation is one of the key image preprocessing steps in object recognition and detection methods. However, the over-segmentation in the smoothly connected homogenous region in an image is the key problem. That would produce redundant complex jagged textures. In this paper, the density peak clustering will be used to reduce the redundant superpixels and highlight the primary textures and contours of the salient objects. Firstly, the grid pixels are extracted as feature points, and the density of each feature point will be defined. Secondly, the cluster centers are extracted with the density peaks. Finally, all the feature points will be clustered by the density peaks. The pixel blocks, which are obtained by the above steps, are superpixels. The method is carried out in the BSDS500 dataset, and the experimental results show that the Boundary Recall (BR) and Achievement Segmentation Accuracy (ASA) are 95.0% and 96.3%, respectively. In addition, the proposed method has better performance in efficiency (30 fps). The comparison experiments show that not only do the superpixel boundaries have good adhesion to the primary textures and contours of the salient objects, but they can also effectively reduce the redundant superpixels in the homogeneous region. MDPI 2021-09-24 /pmc/articles/PMC8512046/ /pubmed/34640692 http://dx.doi.org/10.3390/s21196374 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Xianyi
Peng, Xiafu
Wang, Sun’an
Superpixel Segmentation Based on Grid Point Density Peak Clustering
title Superpixel Segmentation Based on Grid Point Density Peak Clustering
title_full Superpixel Segmentation Based on Grid Point Density Peak Clustering
title_fullStr Superpixel Segmentation Based on Grid Point Density Peak Clustering
title_full_unstemmed Superpixel Segmentation Based on Grid Point Density Peak Clustering
title_short Superpixel Segmentation Based on Grid Point Density Peak Clustering
title_sort superpixel segmentation based on grid point density peak clustering
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512046/
https://www.ncbi.nlm.nih.gov/pubmed/34640692
http://dx.doi.org/10.3390/s21196374
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