Extracting Image Texture and Color Features with MATLAB Implementation

This article introduces an image processing program developed in MATLAB that enables the extraction of texture and color features from images. The program is highly practical, with applications spanning medical image analysis, object recognition, and other computer vision domains. Through this implementation, users can precisely identify distinct regions within an image and extract their characteristic features, facilitating deeper investigation into image properties and their utilization across various applications. The program employs key MATLAB functions such as gray-level co-occurrence matrix (GLCM) for texture analysis and color histogram segmentation for color feature extraction. Users can customize parameters like quantization levels and filter sizes to adapt the program to different image types (e.g., RGB, grayscale) and optimize performance for specific tasks. Algorithmically, it incorporates feature normalization and dimensionality reduction techniques to enhance accuracy and computational efficiency. Moreover, the modular architecture allows for customization—users can integrate additional feature descriptors (e.g., Local Binary Patterns) or modify existing algorithms to suit specialized requirements. This flexibility ensures improved precision and efficiency in real-world scenarios. In summary, this program offers substantial benefits for image analysis and warrants further exploration and application in research and industry contexts.