Image Classification Method Using SVM Kernel

This document presents an image classification methodology employing Support Vector Machine (SVM) kernel techniques. The approach has demonstrated significant effectiveness across various applications by accurately categorizing and labeling digital images. Through kernel-based SVM implementation, we enhance classification precision by effectively capturing complex feature relationships and patterns within image data. The methodology involves training an SVM model using labeled datasets through key steps: feature extraction from images (using techniques like HOG or CNN features), kernel function selection (such as RBF or polynomial kernels), and model optimization via parameter tuning. The trained model subsequently classifies unseen images by computing similarity metrics against learned decision boundaries. This classification approach typically employs sklearn's SVM implementation in Python, where key functions include SVC(kernel='rbf') for model initialization and fit() for training. The algorithm works by mapping input features to higher-dimensional spaces using kernel tricks, enabling linear separation of non-linearly separable data. Implementation advantages include handling high-dimensional image data efficiently and achieving robust performance with limited training samples. This method finds extensive applications in computer vision systems, pattern recognition pipelines, and machine learning frameworks, contributing to improved image analysis accuracy across diverse domains.