Gabor Feature Extraction for Image Classification

Gabor feature extraction is a highly effective approach for image classification tasks. Gabor features are obtained by applying Gabor filters to images, which capture essential texture and edge information through multi-scale and multi-orientation filtering. In implementation, this typically involves creating a bank of Gabor filters with different frequencies and orientations, then convolving them with the input image to generate feature maps.

SVM (Support Vector Machine) algorithm provides a powerful classification method for processing these extracted features. As a supervised learning algorithm, SVM constructs optimal hyperplanes to separate different classes based on labeled training samples, offering strong generalization capabilities. The implementation typically involves feature vector normalization, kernel selection (such as RBF or linear kernels), and parameter optimization through cross-validation.

In computer vision and image processing applications, the combination of Gabor feature extraction and SVM classification has proven to be a widely adopted and effective methodology, particularly for texture analysis and pattern recognition tasks where robust feature representation is crucial.