Automatic Image Distillation with Wavelet Transform and Modified Principal Component Analysis

Data distillation is an emerging research area, attracting the attention of machine learning (ML) and big data scientists and experts. The main goal of a distillation approach is to generate a compact dataset that preserves the essential characteristics of a larger one. In our study, we considered an initial large set of images and developed a novel method for distilling images from the initial set. The method combined discrete wavelet transform (DWT) and modified principal component analysis (M-PCA). Hence, our method first transforms images into vectors of low-band (LL) wavelet coefficients and then applies M-PCA to modify and reduce the number of vectors rather than their dimensionality. This distinguishes our approach from the traditional PCA method. We implemented the new method in Python 3.10 and validated it on public image databases, including Extended YaleB, digit-MNIST, and the ISIC2020. We demonstrated that creating a dictionary from a small set of distilled images and training a sparse representation wavelet-based classifier (SRWC) provides higher accuracy if compared to a classification when the SRWC method is trained with the entire initial training set of images.