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.

基于特征级的融合引起了极大的兴趣。通常，可以在不同的视图、特征或模式中创建数据集。为了提高分类率，通过各种融合方法在不同视图之间共享局部信息。然而，几乎所有的方法都使用视图而不考虑它们的共同方面。在本文中，小波变换被认为是提取视图的高频和低频作为共同方面来提高分类率。分解部分的融合方法基于联合稀疏表示，其中可以考虑多种场景。所提出的方法在三个数据集上进行了测试。与最先进的结果相比，通过该方法获得的结果证明了在数据集方面具有竞争力的性能。