Collaborative Communication: Image Classification using PCA with Grayscale and Texture Features
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Resource Overview
MATLAB program for image classification based on Principal Component Analysis, grayscale/color features, and texture analysis using Gray-Level Co-occurrence Matrix (GLCM), complete with sample images and implementation details.
Detailed Documentation
This documentation presents a MATLAB-based image classification program utilizing Principal Component Analysis (PCA) combined with grayscale/color features and texture characteristics derived from Gray-Level Co-occurrence Matrix (GLCM). The program implements feature extraction through GLCM computation for texture analysis and employs PCA for dimensionality reduction to enhance classification efficiency. The classification algorithm typically involves training a classifier (such as SVM or k-NN) on the reduced feature set. Sample images are provided to demonstrate practical implementation and visualize classification outcomes. The code structure includes modular functions for image preprocessing, feature extraction, PCA transformation, and classification model training. Additional comprehensive tutorials and guidance are available to help users optimize the program for specific image processing and analysis tasks.
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