When frequency domain classification is superior to spatial
Modern image classification models largely rely on local characteristics and feature extraction for their predictions. This focused approach works well in the spatial domain, but reportedly falls short in the frequency domain, even though both representations contain the same information. This study demonstrates that the significant reduction in accuracy in the frequency domain stems from the lack of local features. Locally swapping data in the frequency domain significantly decreases its inverse spatial domain accuracy, even below that of the frequency domain; however, the frequency domain accuracy remains relatively constant. Moreover, the use of datasets containing global features in the spatial domain yields higher accuracy in the frequency domain. Using these results, a hybrid model is constructed, combining one model trained only in the frequency domain and another trained only in the spatial domain to achieve higher classification accuracy. The proposed visual geometry group (VGG) and vision transformer (ViT) architectures are trained on the ImageNet-100, WikiArt, DTD, FMD, PSVRT, and KTH-TIPS2-b datasets and evaluated.