Image Classification Based on Block Partition Feature Extraction Using SVM

This project comprises both research paper development and implementation. The research paper focuses on using Support Vector Machines (SVM) for image classification based on block partition feature extraction, and we have implemented corresponding MATLAB code to handle image partitioning, feature extraction, and clustering. During implementation, we utilized MATLAB 6.5 for development. The project involves multiple steps including data preprocessing, feature selection, and model training. Key MATLAB functions employed include image processing routines for block division, feature extraction algorithms for pattern recognition, and clustering methods for data organization. Through this research, we aim to further explore image classification methodologies and techniques while improving classification accuracy and efficiency. The implementation demonstrates practical approaches to handling image data segmentation and feature vector computation using MATLAB's image processing toolbox and statistical functions.