MATLAB Code Implementation for Image Processing Toolbox

MATLAB serves as a powerful scientific computing tool, with its Image Processing Toolbox providing extensive algorithm implementations covering the complete workflow from preprocessing to advanced analysis. In complex image recognition tasks, it is often necessary to combine multiple machine learning methods to enhance processing effectiveness.

Principal Component Analysis (PCA) is a commonly used dimensionality reduction technique in image processing. By calculating eigenvectors of the covariance matrix, high-dimensional pixel data is projected into a low-dimensional feature space, preserving essential information while reducing computational load. In MATLAB, this can be implemented using built-in functions like pca() for eigenvalue decomposition and reconstructing data through component analysis.

Artificial Neural Networks (ANN) achieve nonlinear modeling by simulating the connections of human brain neurons. For image classification tasks, multilayer perceptron architectures are typically employed, where the input layer receives flattened pixel matrices, hidden layers perform feature abstraction, and the output layer provides classification probabilities. MATLAB's Deep Learning Toolbox supports rapid network construction with functions like trainNetwork() and automatically handles backpropagation processes.

Support Vector Machines (SVM) excel in small-sample image classification, where kernel functions effectively solve nonlinear separation problems. MATLAB's implementation offers multiple kernel function choices through functions like fitcsvm(), finding optimal hyperplanes to maximize classification margins, making it particularly suitable for high-precision scenarios like texture analysis. These three methods can be used individually or combined into hybrid models based on specific requirements.