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Feature fusion based on joint sparse representations and wavelets for multiview classification

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journal contribution
posted on 2022-11-22, 21:12 authored by Younes Akbari, Omar Elharrouss, Somaya Al-Maadeed

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.

Other Information

Published in: Pattern Analysis and Applications
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: http://dx.doi.org/10.1007/s10044-022-01110-2

History

Language

  • English

Publisher

Springer Science and Business Media LLC

Publication Year

  • 2022

Institution affiliated with

  • Qatar University

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