Pattern Recognition in Image Processing Using Genetic Algorithm

This MATLAB code implements pattern recognition in image processing using genetic algorithms. The implementation consists of the following key computational stages:

1. Image Reading and Preprocessing: The code begins with image acquisition using MATLAB's imread() function, followed by preprocessing steps such as noise removal using median filtering and image smoothing through Gaussian filters to enhance data quality.

2. Feature Extraction: We employ multiple feature extraction techniques including Gray-Level Co-occurrence Matrix (GLCM) for texture analysis using graycoprops() function, and Local Binary Patterns (LBP) for local texture descriptor extraction through efficient neighborhood operations.

3. Genetic Algorithm Parameter Configuration: The algorithm initializes GA parameters including population size (typically 50-100 individuals), crossover rate (0.7-0.9), and mutation rate (0.01-0.1) using MATLAB's Global Optimization Toolbox gaoptimset function.

4. Genetic Algorithm Optimization: The core optimization process utilizes MATLAB's ga() function to evolve feature weights and recognition parameters, implementing fitness evaluation based on classification accuracy and selection operations using tournament selection or roulette wheel methods.

5. Result Analysis and Evaluation: Final performance validation employs confusion matrices, ROC curves, and accuracy metrics through functions like confusionmat() and perfcurve() to quantify recognition effectiveness.

By integrating genetic algorithms with image processing for pattern recognition, this implementation achieves enhanced pattern detection accuracy through evolutionary optimization of feature selection and classifier parameters. The modular code structure allows for easy adaptation to various image recognition tasks.