Texture Image Segmentation Implementation in MATLAB

Texture image segmentation is a crucial task in computer vision, commonly used to partition regions with similar texture characteristics in images. Implementing this process in MATLAB typically involves the following core steps: Gaussian Filtering Preprocessing Gaussian filtering is employed to smooth images and reduce noise interference with texture features. By adjusting the filter's standard deviation (σ), the smoothing degree can be controlled to preserve essential texture structures. This preprocessing step helps subsequent clustering algorithms more accurately identify different texture regions. In MATLAB implementation, this can be achieved using the imgaussfilt() function with customizable sigma parameters. Texture Feature Extraction While specific extraction methods weren't explicitly mentioned by the user, common texture features include Gray-Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), or Gabor filter responses. These features quantify local texture properties of images, providing data support for subsequent clustering. MATLAB offers functions like graycomatrix() for GLCM features and gaborFilterBank() for Gabor filter implementations. K-means Clustering Segmentation Based on extracted texture features, the K-means algorithm partitions pixels into a specified number (K value) of clusters. Each cluster represents a texture category, with cluster centers serving as characteristic representatives of that category. The selection of K value should consider practical scenarios and can be optimized using methods like the elbow method or silhouette coefficient. MATLAB's kmeans() function can be directly applied to feature vectors for this purpose. Region Boundary Annotation After clustering completion, boundaries between different regions can be extracted using edge detection algorithms (such as Sobel or Canny) or morphological operations (like dilation-erosion). These boundaries are ultimately overlaid on the original image as lines to visualize segmentation results. The edge() function in MATLAB provides multiple edge detection methods for boundary extraction. Extended Applications For multi-scale texture processing, pyramid models can be combined with hierarchical clustering approaches. For dynamic textures (like video sequences), temporal features can be incorporated to extend K-means to dynamic clustering scenarios. Accompanying test images, documentation, and PPT materials may include parameter tuning guidelines or practical cases to help users quickly reproduce and adjust the algorithm.