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A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition
It is a challenging problem to classify multi-dimensional data with complex intrinsic geometry inherent, such as human gesture recognition based on videos. In particular, manifold structure is a good way to characterize intrinsic geometry of multi-dimensional data. The recently proposed sparse codin...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044265/ https://www.ncbi.nlm.nih.gov/pubmed/35494827 http://dx.doi.org/10.7717/peerj-cs.923 |
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author | Wang, Yuping Zhang, Junfei |
author_facet | Wang, Yuping Zhang, Junfei |
author_sort | Wang, Yuping |
collection | PubMed |
description | It is a challenging problem to classify multi-dimensional data with complex intrinsic geometry inherent, such as human gesture recognition based on videos. In particular, manifold structure is a good way to characterize intrinsic geometry of multi-dimensional data. The recently proposed sparse coding on Grassmann manifold shows high discriminative power in many visual classification tasks. It represents videos on Grassmann manifold using Singular Value Decomposition (SVD) of the data matrix by vectorizing each image in videos, while vectorization destroys the spatial structure of videos. To keep the spatial structure of videos, they can be represented as the form of data tensor. In this paper, we firstly represent human gesture videos on product Grassmann manifold (PGM) by Higher Order Singular Value Decomposition (HOSVD) of data tensor. Each factor manifold characterizes features of human gesture video from different perspectives and can be understood as appearance, horizontal motion and vertical motion of human gesture video respectively. We then propose a weighted sparse coding model on PGM, where weights can be understood as modeling the importance of factor manifolds. Furthermore, we propose an optimization algorithm for learning coding coefficients by embedding each factor Grassmann manifold into symmetric matrices space. Finally, we give a classification algorithm, and experimental results on three public datasets show that our method is competitive to some relevant excellent methods. |
format | Online Article Text |
id | pubmed-9044265 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90442652022-04-28 A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition Wang, Yuping Zhang, Junfei PeerJ Comput Sci Computer Vision It is a challenging problem to classify multi-dimensional data with complex intrinsic geometry inherent, such as human gesture recognition based on videos. In particular, manifold structure is a good way to characterize intrinsic geometry of multi-dimensional data. The recently proposed sparse coding on Grassmann manifold shows high discriminative power in many visual classification tasks. It represents videos on Grassmann manifold using Singular Value Decomposition (SVD) of the data matrix by vectorizing each image in videos, while vectorization destroys the spatial structure of videos. To keep the spatial structure of videos, they can be represented as the form of data tensor. In this paper, we firstly represent human gesture videos on product Grassmann manifold (PGM) by Higher Order Singular Value Decomposition (HOSVD) of data tensor. Each factor manifold characterizes features of human gesture video from different perspectives and can be understood as appearance, horizontal motion and vertical motion of human gesture video respectively. We then propose a weighted sparse coding model on PGM, where weights can be understood as modeling the importance of factor manifolds. Furthermore, we propose an optimization algorithm for learning coding coefficients by embedding each factor Grassmann manifold into symmetric matrices space. Finally, we give a classification algorithm, and experimental results on three public datasets show that our method is competitive to some relevant excellent methods. PeerJ Inc. 2022-03-16 /pmc/articles/PMC9044265/ /pubmed/35494827 http://dx.doi.org/10.7717/peerj-cs.923 Text en ©2022 Wang and Zhang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Computer Vision Wang, Yuping Zhang, Junfei A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition |
title | A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition |
title_full | A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition |
title_fullStr | A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition |
title_full_unstemmed | A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition |
title_short | A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition |
title_sort | weighted sparse coding model on product grassmann manifold for video-based human gesture recognition |
topic | Computer Vision |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044265/ https://www.ncbi.nlm.nih.gov/pubmed/35494827 http://dx.doi.org/10.7717/peerj-cs.923 |
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