Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
As an important research direction in image and video processing, set-based video recognition requires speed and accuracy. However, the existing static modeling methods focus on computational speed but ignore accuracy, whereas the dynamic modeling methods are higher-accuracy but ignore the computati...
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ftdoajarticles:oai:doaj.org/article:8e0f365212444a8a91a8edd2cfffd676 2023-07-02T03:32:05+02:00 Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition Xizhan Gao Kang Wei Jia Li Ziyu Shi Hui Zhao Sijie Niu 2023-05-01T00:00:00Z https://doi.org/10.3390/app13116383 https://doaj.org/article/8e0f365212444a8a91a8edd2cfffd676 EN eng MDPI AG https://www.mdpi.com/2076-3417/13/11/6383 https://doaj.org/toc/2076-3417 doi:10.3390/app13116383 2076-3417 https://doaj.org/article/8e0f365212444a8a91a8edd2cfffd676 Applied Sciences, Vol 13, Iss 6383, p 6383 (2023) set-based video recognition image set classification manifold learning fast and accurate classification discriminative dictionary learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 article 2023 ftdoajarticles https://doi.org/10.3390/app13116383 2023-06-11T00:33:59Z As an important research direction in image and video processing, set-based video recognition requires speed and accuracy. However, the existing static modeling methods focus on computational speed but ignore accuracy, whereas the dynamic modeling methods are higher-accuracy but ignore the computational speed. Combining these two types of methods to obtain fast and accurate recognition results remains a challenging problem. Motivated by this, in this study, a novel Manifolds-based Low-Rank Dictionary Pair Learning (MbLRDPL) method was developed for a set-based video recognition/image set classification task. Specifically, each video or image set was first modeled as a covariance matrix or linear subspace, which can be seen as a point on a Riemannian manifold. Second, the proposed MbLRDPL learned discriminative class-specific synthesis and analysis dictionaries by clearly imposing the nuclear norm on the synthesis dictionaries. The experimental results show that our method achieved the best classification accuracy (100%, 72.16%, 95%) on three datasets with the fastest computing time, reducing the errors of state-of-the-art methods (JMLC, DML, CEBSR) by 0.96–75.69%. Article in Journal/Newspaper DML Directory of Open Access Journals: DOAJ Articles Applied Sciences 13 11 6383 |
institution |
Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
set-based video recognition image set classification manifold learning fast and accurate classification discriminative dictionary learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
spellingShingle |
set-based video recognition image set classification manifold learning fast and accurate classification discriminative dictionary learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Xizhan Gao Kang Wei Jia Li Ziyu Shi Hui Zhao Sijie Niu Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition |
topic_facet |
set-based video recognition image set classification manifold learning fast and accurate classification discriminative dictionary learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
description |
As an important research direction in image and video processing, set-based video recognition requires speed and accuracy. However, the existing static modeling methods focus on computational speed but ignore accuracy, whereas the dynamic modeling methods are higher-accuracy but ignore the computational speed. Combining these two types of methods to obtain fast and accurate recognition results remains a challenging problem. Motivated by this, in this study, a novel Manifolds-based Low-Rank Dictionary Pair Learning (MbLRDPL) method was developed for a set-based video recognition/image set classification task. Specifically, each video or image set was first modeled as a covariance matrix or linear subspace, which can be seen as a point on a Riemannian manifold. Second, the proposed MbLRDPL learned discriminative class-specific synthesis and analysis dictionaries by clearly imposing the nuclear norm on the synthesis dictionaries. The experimental results show that our method achieved the best classification accuracy (100%, 72.16%, 95%) on three datasets with the fastest computing time, reducing the errors of state-of-the-art methods (JMLC, DML, CEBSR) by 0.96–75.69%. |
format |
Article in Journal/Newspaper |
author |
Xizhan Gao Kang Wei Jia Li Ziyu Shi Hui Zhao Sijie Niu |
author_facet |
Xizhan Gao Kang Wei Jia Li Ziyu Shi Hui Zhao Sijie Niu |
author_sort |
Xizhan Gao |
title |
Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition |
title_short |
Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition |
title_full |
Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition |
title_fullStr |
Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition |
title_full_unstemmed |
Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition |
title_sort |
manifolds-based low-rank dictionary pair learning for efficient set-based video recognition |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/app13116383 https://doaj.org/article/8e0f365212444a8a91a8edd2cfffd676 |
genre |
DML |
genre_facet |
DML |
op_source |
Applied Sciences, Vol 13, Iss 6383, p 6383 (2023) |
op_relation |
https://www.mdpi.com/2076-3417/13/11/6383 https://doaj.org/toc/2076-3417 doi:10.3390/app13116383 2076-3417 https://doaj.org/article/8e0f365212444a8a91a8edd2cfffd676 |
op_doi |
https://doi.org/10.3390/app13116383 |
container_title |
Applied Sciences |
container_volume |
13 |
container_issue |
11 |
container_start_page |
6383 |
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1770271574693249024 |