Collaborative Communication for Image Classification: PCA and Feature Fusion with MATLAB Implementation

Image classification represents one of the core tasks in computer vision, and classification methods based on Principal Component Analysis (PCA) combined with image features (such as grayscale and texture) constitute a classical yet effective technical approach. This article demonstrates how to implement this pipeline using MATLAB, enhancing classification efficiency through dimensionality reduction and feature fusion.

Core Methodology Feature Extraction Grayscale Features: Directly extract statistical measures from image grayscale values (e.g., mean, variance) as foundational features using MATLAB's mean() and var() functions. Texture Features: Calculate texture indicators like contrast, energy, and correlation through Gray-Level Co-occurrence Matrix (GLCM) using graycomatrix() and graycoprops() functions to characterize local image structures.

Dimensionality Reduction Apply PCA to high-dimensional features (e.g., concatenated grayscale + texture features) using pca() function, retaining principal variance components to reduce computational redundancy and suppress noise. The algorithm computes eigenvalues and eigenvectors to identify feature directions with maximum variance.

Classification Modeling Reduced-dimension features can be fed into classifiers (e.g., SVM, KNN) for training and prediction. MATLAB's Classification Learner toolbox enables rapid performance validation of different models through interactive interface and cross-validation techniques.

Technical Implementation Details GLCM Optimization: Appropriately set GLCM's offset directions and distance parameters (e.g., 0°, 45°, 90°, 135°) using graycomatrix()'s 'Offset' parameter to capture multi-directional texture information. PCA Whitening: Standardize data using zscore() before PCA when feature scales differ significantly to prevent variance dominance issues. Sample Image Selection: Choose categories with distinct illumination/texture variations (e.g., fabrics, natural scenes) to effectively validate feature performance through comparative analysis.

This methodology applies to medical imaging, remote sensing, and similar domains, though note that feature engineering heavily depends on domain-specific knowledge for optimal results.