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An Efficient Hybrid Linear Clustering Superpixel Decomposition Framework for Traffic Scene Semantic Segmentation
Superpixel decomposition could reconstruct an image through meaningful fragments to extract regional features, thus boosting the performance of advanced computer vision tasks. To further optimize the computational efficiency as well as segmentation quality, a novel framework is proposed to generate...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861474/ https://www.ncbi.nlm.nih.gov/pubmed/36679799 http://dx.doi.org/10.3390/s23021002 |
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author | Zhong, Dan Li, Tiehu Dong, Yuxuan |
author_facet | Zhong, Dan Li, Tiehu Dong, Yuxuan |
author_sort | Zhong, Dan |
collection | PubMed |
description | Superpixel decomposition could reconstruct an image through meaningful fragments to extract regional features, thus boosting the performance of advanced computer vision tasks. To further optimize the computational efficiency as well as segmentation quality, a novel framework is proposed to generate superpixels from the perspective of hybridizing two existing linear clustering frameworks. Instead of conventional grid sampling seeds for region clustering, a fast convergence strategy is first introduced to center the final superpixel clusters, which is based on an accelerated convergence strategy. Superpixels are then generated from a center-fixed online average clustering, which adopts region growing to label all pixels in an efficient one-pass manner. The experiments verify that the integration of this two-step implementation could generate a synergistic effect and that it becomes more well-rounded than each single method. Compared with other state-of-the-art superpixel algorithms, the proposed framework achieves a comparable overall performance in terms of segmentation accuracy, spatial compactness and running efficiency; moreover, an application on image segmentation verifies its facilitation for traffic scene analysis. |
format | Online Article Text |
id | pubmed-9861474 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98614742023-01-22 An Efficient Hybrid Linear Clustering Superpixel Decomposition Framework for Traffic Scene Semantic Segmentation Zhong, Dan Li, Tiehu Dong, Yuxuan Sensors (Basel) Article Superpixel decomposition could reconstruct an image through meaningful fragments to extract regional features, thus boosting the performance of advanced computer vision tasks. To further optimize the computational efficiency as well as segmentation quality, a novel framework is proposed to generate superpixels from the perspective of hybridizing two existing linear clustering frameworks. Instead of conventional grid sampling seeds for region clustering, a fast convergence strategy is first introduced to center the final superpixel clusters, which is based on an accelerated convergence strategy. Superpixels are then generated from a center-fixed online average clustering, which adopts region growing to label all pixels in an efficient one-pass manner. The experiments verify that the integration of this two-step implementation could generate a synergistic effect and that it becomes more well-rounded than each single method. Compared with other state-of-the-art superpixel algorithms, the proposed framework achieves a comparable overall performance in terms of segmentation accuracy, spatial compactness and running efficiency; moreover, an application on image segmentation verifies its facilitation for traffic scene analysis. MDPI 2023-01-15 /pmc/articles/PMC9861474/ /pubmed/36679799 http://dx.doi.org/10.3390/s23021002 Text en © 2023 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 Zhong, Dan Li, Tiehu Dong, Yuxuan An Efficient Hybrid Linear Clustering Superpixel Decomposition Framework for Traffic Scene Semantic Segmentation |
title | An Efficient Hybrid Linear Clustering Superpixel Decomposition Framework for Traffic Scene Semantic Segmentation |
title_full | An Efficient Hybrid Linear Clustering Superpixel Decomposition Framework for Traffic Scene Semantic Segmentation |
title_fullStr | An Efficient Hybrid Linear Clustering Superpixel Decomposition Framework for Traffic Scene Semantic Segmentation |
title_full_unstemmed | An Efficient Hybrid Linear Clustering Superpixel Decomposition Framework for Traffic Scene Semantic Segmentation |
title_short | An Efficient Hybrid Linear Clustering Superpixel Decomposition Framework for Traffic Scene Semantic Segmentation |
title_sort | efficient hybrid linear clustering superpixel decomposition framework for traffic scene semantic segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861474/ https://www.ncbi.nlm.nih.gov/pubmed/36679799 http://dx.doi.org/10.3390/s23021002 |
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