Publication Title
IEEE Access
Document Type
Article
Abstract/Description
In this study, we propose a novel sparse representation learning method in the Quaternion Wavelet (QW) domain for multi-class image classification. The proposed method takes advantages from: i) the QW decomposition, which promotes sparsity and provides structural information about the image data while allowing approximate shift-invariance, to extract meaningful features from low-frequency QW subbands, ii) the dimensionality reduction method using Principal Component Analysis (PCA) for reducing the complexity of the problem, and iii) the sparse representation of the generated QW features to efficiently learn and capture the meaningful and compact information of this data. After the QW decomposition, the features extracted from low-frequency image sub-bands information are projected, by the PCA, into a new feature space with lower dimensionality. The features extracted from the training samples are used to construct a dictionary, while the features of the test samples are sparsely coded for the classification step. The sparse coding problem is formulated in a QW Least Absolute Shrinkage and Selection Operator (QWLasso) model applying quaternion l1 minimization. A novel Quaternion Fast Iterative Shrinkage-Thresholding Algorithm (QFISTA) is developed to solve the QWLasso model. The experiments conducted on various public image datasets validated that the proposed method possesses higher accuracy, sparsity, and robustness in comparison with several contemporary methods in the field including Neural Networks.
Department
Mathematics
First Page
31548
Last Page
31560
DOI
10.1109/ACCESS.2022.3159701
Volume
10
ISSN
2169-3536
Date
3-25-2022
Citation Information
Ngo, Long H.; Sirakov, Nikolay Metodiev; Luong, Marie; and Viennet, Emmanuel, "Image Classification Based on Sparse Representation in the Quaternion Wavelet Domain" (2022). Faculty Publications. 231.
https://digitalcommons.tamuc.edu/cose-faculty-publications/231
