Color Texture Segmentation Using Gabor Filter

This document presents a code implementation for color texture segmentation using Gabor filters. The algorithm begins with initial segmentation using k-means clustering, which provides a preliminary grouping of pixels based on color features. This initial segmentation serves as a foundation for further refinement through texture analysis. Gabor filters are localized frequency-based filters capable of detecting texture patterns across multiple orientations and scales. By applying Gabor filters at various scales and orientations, we extract comprehensive texture features from the image. These multiscale texture responses are then utilized to enhance the segmentation accuracy beyond simple color-based clustering. The code implementation incorporates several key image processing techniques: - Color space conversion (typically from RGB to more perceptually uniform spaces like Lab or HSV) - Image smoothing operations to reduce noise and improve segmentation stability - Multi-scale Gabor filter bank creation with configurable parameters (wavelength, orientation, bandwidth) - Feature extraction combining both color information and texture responses - Post-processing techniques to refine segmentation boundaries and remove artifacts The algorithm workflow involves: computing Gabor filter responses across multiple scales/orientations, combining texture features with color information, performing initial k-means clustering, and applying region-growing or boundary refinement techniques for final segmentation. This approach achieves robust texture segmentation while maintaining computational efficiency through optimized filter implementation and feature dimensionality reduction techniques.