Embedding vector field into image features to enhance classification

• Published in: Journal of computational and applied mathematics 2024-05, Vol.441, p.115685, Article 115685
• Main Authors: Sirakov, N.M., Bowden, A., Chen, M., Ngo, L.H., Luong, M.
• Format: Article
• Language: English
• Subjects: Features; Gradient vector fields; Poisson equation; Sparse representation classification
• ISSN: 0377-0427; 1879-1778
• DOI: 10.1016/j.cam.2023.115685

Classical methods, including sparse representation classification (SRC) and neural networks (NNs), classify image object(s) using features like intensity, color, texture, and geometry extracted from the image. For the purpose of classification the present study proposes to augment the set of image features with vector field (VF) features, like singular points (SPs) and trajectories. To generate VFs with such features, our approach solves the Poisson Image (PI) equation. Using its solution uˆ(x,y), our approach defines two functions ϕˆ(x,y) and ψˆ(x,y) and develops two Poisson gradient VFs (PGVFs) ∇ϕˆ(x,y) and ∇ψˆ(x,y), used to embed features into a database of images. The embedding of VF features into image objects constitutes the main novelty of this study. The advantage that comes from the novelty is increased statistics of machine learning (ML) classifiers. To validate the advantage, the PGVFs ∇ϕˆ(x,y) and ∇ψˆ(x,y) were embedded into the public image databases COIL100, YaleB, ISIC2018, and ISIC2020. The original and the eight VF covered databases were classified with two ML classifiers: sparse representation wavelet classification (SRWC) and SRC in the quaternion wavelets (SRCQW) domain. The results obtained are presented in the paper and confirm the claimed advantage.