Feature fusion based on joint sparse representations and wavelets for multiview classification
<p>Feature-level-based fusion has attracted much interest. Generally, a dataset can be created in different views, features, or modalities. To improve the classification rate, local information is shared among different views by various fusion methods. However, almost all the methods use the v...
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2022
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| _version_ | 1864513567962693632 |
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| author | Younes Akbari (14150781) |
| author2 | Omar Elharrouss (14150784) Somaya Al-Maadeed (5178131) |
| author2_role | author author |
| author_facet | Younes Akbari (14150781) Omar Elharrouss (14150784) Somaya Al-Maadeed (5178131) |
| author_role | author |
| dc.creator.none.fl_str_mv | Younes Akbari (14150781) Omar Elharrouss (14150784) Somaya Al-Maadeed (5178131) |
| dc.date.none.fl_str_mv | 2022-11-22T21:12:41Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10044-022-01110-2 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Feature_fusion_based_on_joint_sparse_representations_and_wavelets_for_multiview_classification/21597132 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Artificial intelligence Computer vision and multimedia computation Artificial Intelligence Computer Vision and Pattern Recognition |
| dc.title.none.fl_str_mv | Feature fusion based on joint sparse representations and wavelets for multiview classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Feature-level-based fusion has attracted much interest. Generally, a dataset can be created in different views, features, or modalities. To improve the classification rate, local information is shared among different views by various fusion methods. However, almost all the methods use the views without considering their common aspects. In this paper, wavelet transform is considered to extract high and low frequencies of the views as common aspects to improve the classification rate. The fusion method for the decomposed parts is based on joint sparse representation in which a number of scenarios can be considered. The presented approach is tested on three datasets. The results obtained by this method prove competitive performance in terms of the datasets compared to the state-of-the-art results.</p><h2>Other Information</h2> <p> Published in: Pattern Analysis and Applications<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10044-022-01110-2" target="_blank">http://dx.doi.org/10.1007/s10044-022-01110-2</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_706ba500dbbb59f9c13220e33b8e91f8 |
| identifier_str_mv | 10.1007/s10044-022-01110-2 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597132 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Feature fusion based on joint sparse representations and wavelets for multiview classificationYounes Akbari (14150781)Omar Elharrouss (14150784)Somaya Al-Maadeed (5178131)Artificial intelligenceComputer vision and multimedia computationArtificial IntelligenceComputer Vision and Pattern Recognition<p>Feature-level-based fusion has attracted much interest. Generally, a dataset can be created in different views, features, or modalities. To improve the classification rate, local information is shared among different views by various fusion methods. However, almost all the methods use the views without considering their common aspects. In this paper, wavelet transform is considered to extract high and low frequencies of the views as common aspects to improve the classification rate. The fusion method for the decomposed parts is based on joint sparse representation in which a number of scenarios can be considered. The presented approach is tested on three datasets. The results obtained by this method prove competitive performance in terms of the datasets compared to the state-of-the-art results.</p><h2>Other Information</h2> <p> Published in: Pattern Analysis and Applications<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10044-022-01110-2" target="_blank">http://dx.doi.org/10.1007/s10044-022-01110-2</a></p>2022-11-22T21:12:41ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10044-022-01110-2https://figshare.com/articles/journal_contribution/Feature_fusion_based_on_joint_sparse_representations_and_wavelets_for_multiview_classification/21597132CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215971322022-11-22T21:12:41Z |
| spellingShingle | Feature fusion based on joint sparse representations and wavelets for multiview classification Younes Akbari (14150781) Artificial intelligence Computer vision and multimedia computation Artificial Intelligence Computer Vision and Pattern Recognition |
| status_str | publishedVersion |
| title | Feature fusion based on joint sparse representations and wavelets for multiview classification |
| title_full | Feature fusion based on joint sparse representations and wavelets for multiview classification |
| title_fullStr | Feature fusion based on joint sparse representations and wavelets for multiview classification |
| title_full_unstemmed | Feature fusion based on joint sparse representations and wavelets for multiview classification |
| title_short | Feature fusion based on joint sparse representations and wavelets for multiview classification |
| title_sort | Feature fusion based on joint sparse representations and wavelets for multiview classification |
| topic | Artificial intelligence Computer vision and multimedia computation Artificial Intelligence Computer Vision and Pattern Recognition |