Gabor Wavelet Transform-Based Feature Extraction for Input Images

Feature extraction for input images can be performed using Gabor wavelet transform-based methods. Gabor wavelet transform is an effective signal analysis technique that captures texture and edge characteristics in images. By applying Gabor wavelet transform, we obtain image responses at different frequencies and orientations, enabling the extraction of distinctive feature information. These features are valuable for applications such as image recognition, classification, and detection.

Implementation typically involves creating a Gabor filter bank with multiple scales and orientations. Key functions include generating Gabor kernels with specific parameters (wavelength, orientation, phase offset, and bandwidth), convolving these kernels with the input image, and extracting feature vectors from the filtered responses. The algorithm can be optimized using parallel processing for real-time applications, and feature dimensionality reduction techniques like PCA may be applied post-extraction. Therefore, Gabor wavelet transform-based feature extraction holds significant application value in the field of image processing, particularly for texture analysis and pattern recognition tasks.