Thông tin tài liệu
Nhan đề : |
Feature fusion based on joint sparse representations and wavelets for multiview classification |
Tác giả : |
Younes, Akbari Omar, Elharrouss Somaya, Al-Maadeed |
Năm xuất bản : |
2022 |
Nhà xuất bản : |
Springer |
Tóm tắt : |
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. |
Mô tả: |
CC BY |
URI: |
https://link.springer.com/article/10.1007/s10044-022-01110-2 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8290 |
Bộ sưu tập |
OER - Công nghệ thông tin |
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