Gabor Wavelet-Based Image Texture Feature Extraction

Gabor wavelets are widely used tools in image processing and computer vision, particularly effective for texture feature extraction. By combining orientation and scale selection properties, they efficiently capture local texture information from images, demonstrating superior performance in applications such as facial recognition and fingerprint identification.

The core concept of Gabor wavelets involves simulating the receptive fields of human visual systems to perform multi-scale and multi-orientation filtering on images. Each Gabor filter can be mathematically represented as the product of a sinusoidal wave and a Gaussian function, achieving optimal localization in both spatial and frequency domains.

Implementing Gabor wavelet texture feature extraction in MATLAB typically involves the following key steps: First, construct a Gabor filter bank by selecting appropriate scale and orientation parameters using functions like gabor or custom implementations. Next, apply the filter bank to input images through convolution operations (using imfilter or conv2) to obtain responses across different orientations and scales. Finally, perform feature encoding by calculating statistical measures such as mean values, variances, or energy metrics from the filtered responses to form the final texture feature vector.

This approach proves highly effective in extracting local structural information from images, particularly when processing periodic textures. Gabor wavelets significantly enhance classification and recognition accuracy through their ability to decompose texture patterns into oriented frequency components.