MATLAB Implementation of SVM-Based Image Classification

This implementation leverages the Support Vector Machine (SVM) algorithm for binary image classification tasks. SVM is a classical machine learning algorithm that identifies an optimal hyperplane to separate data into two distinct classes. For image classification, the process involves extracting relevant features from images (such as color histograms, texture descriptors, or deep learning features) and using them as input data for SVM training. The MATLAB implementation typically utilizes functions like fitcsvm for model training, where kernel functions (linear, RBF, or polynomial) can be specified to handle non-linearly separable data. Key steps include feature standardization using zscore, hyperparameter tuning via cross-validation with crossval, and evaluation through metrics like accuracy computed with predict and confusionmat. By training an SVM classifier on extracted image features, the system achieves accurate binary classification, effectively meeting image categorization objectives.