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

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 views by various methods. However, almost all methods use without considering their common aspects. In this paper, wavelet transform considered to extract high and low frequencies of as aspects rate. The method for decomposed parts based on joint sparse representation which number scenarios considered. presented approach tested three datasets. results obtained prove competitive performance terms datasets compared state-of-the-art results.