Gabor Wavelet Feature Extraction and SVM Classification Implementation

This MATLAB source code implements Gabor wavelet feature extraction followed by classification using the Support Vector Machine (SVM) algorithm. Gabor wavelets serve as an effective feature extraction method that captures texture and edge information from images through multi-scale and multi-orientation filtering. The implementation typically involves creating Gabor filter banks with specific parameters (wavelength, orientation, bandwidth) and convolving them with input images to generate feature vectors. SVM is a powerful supervised learning algorithm that constructs optimal hyperplanes to separate different classes in feature space. The MATLAB implementation likely utilizes libsvm or fitcsvm functions for training and classification, employing kernel functions (linear, RBF, polynomial) to handle non-linear separability. By combining Gabor wavelet feature extraction with SVM classification, this code achieves accurate image classification through a robust pipeline: image preprocessing → Gabor feature extraction → feature normalization → SVM model training → classification prediction. The implementation includes parameter optimization for Gabor filters and SVM hyperparameters to enhance classification performance. This source code provides researchers and developers with a comprehensive framework to understand and apply Gabor wavelet feature extraction and SVM classification algorithms in practical image analysis tasks, featuring modular code structure with clear function separation for filter generation, feature calculation, and classification modules.