Kernel-based Algorithms for Image Classification: A Review

Abstract The field of kernel learning methods has made significant strides since their inception in the 1990s. These methods involve mapping data from the original input space to a higher-dimensional feature space using an implicitly determined nonlinear mapping through a kernel function. In the feature space, a linear model is constructed, enabling the fusion of statistical information and the geometric structure of data samples. Kernel methods have demonstrated their effectiveness in various computer vision applications, including remote sensing image analysis, face recognition, and online scene classification. However, traditional kernel methods face challenges in image classification, such as computational complexity and the design of appropriate kernel functions for complex image features. To address these limitations, we explore two approaches: online kernel learning and the integration of deep learning architectures with kernel methods. We present advancements and applications in image classification tasks, aiming to provide a comprehensive overview of kernel learning methods and identify future research directions in this domain.