MATLAB-Based Image Classification Software

This image classification software, developed using MATLAB programming language, enables efficient classification and matching recognition of objects within images. The software incorporates multiple functional modules including image preprocessing, feature extraction, model training, and testing phases. Key implementation aspects involve:

Image preprocessing utilizes MATLAB's Image Processing Toolbox functions like imresize for normalization and imfilter for noise reduction. Feature extraction algorithms may employ techniques such as HOG (Histogram of Oriented Gradients) using extractHOGFeatures function or deep learning features through pretrained CNNs like AlexNet. The classification engine typically implements machine learning models (SVM via fitcsvm, KNN via fitcknn) or neural networks using Deep Learning Toolbox.

Supporting multiple image formats (JPEG, PNG, TIFF) through imread function, the software features an intuitive GUI developed using App Designer for user-friendly operation. The system demonstrates practical value in both scientific research and engineering applications by accelerating image analysis workflows through automated classification pipelines. Batch processing capabilities allow efficient handling of large datasets using imageDatastore function, while customizable parameters enable adaptation to specific domain requirements.

Code architecture follows modular design principles, separating preprocessing, feature extraction, and classification into independent functions with clear input/output interfaces. This design facilitates maintenance and extension of functionality, making it suitable for academic and industrial image analysis tasks.