Feature fusion based on joint sparse representations and wavelets for multiview classification
Author | Akbari, Younes |
Author | Elharrouss, Omar |
Author | Al-Maadeed, Somaya |
Available date | 2023-02-23T09:13:04Z |
Publication Date | 2022 |
Publication Name | Pattern Analysis and Applications |
Resource | Scopus |
Abstract | 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. |
Sponsor | This publication was made possible by NPRP grant # NPRP12S-0312-190332 from Qatar National Research Fund (a member of Qatar Foundation). The statement made herein are solely the responsibility of the authors. |
Language | en |
Publisher | Springer Science and Business Media Deutschland GmbH |
Subject | Feature extraction Fusion method Wavelet transform |
Type | Article |
Pagination | - |
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Computer Science & Engineering [2281 items ]