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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...

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Detalles Bibliográficos
Autores principales: Zhong, Dan, Li, Tiehu, Dong, Yuxuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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