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المؤلفAkbari, Younes
المؤلفElharrouss, Omar
المؤلفAl-Maadeed, Somaya
تاريخ الإتاحة2023-02-23T09:13:04Z
تاريخ النشر2022
اسم المنشورPattern Analysis and Applications
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/s10044-022-01110-2
معرّف المصادر الموحدhttp://hdl.handle.net/10576/40329
الملخصFeature-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.
راعي المشروعThis 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.
اللغةen
الناشرSpringer Science and Business Media Deutschland GmbH
الموضوعFeature extraction
Fusion method
Wavelet transform
العنوانFeature fusion based on joint sparse representations and wavelets for multiview classification
النوعArticle
الصفحات-


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