Feature Extraction from Images Using Convolutional Neural Networks

In this research, we implement Convolutional Neural Networks (CNNs) using the MATLAB programming language. CNNs represent a deep learning algorithm widely applied in image processing and computer vision domains. Our implementation focuses on utilizing CNNs for image feature extraction, which serves as a fundamental method for analyzing and recognizing image contents. Through CNN processing, we can extract key features from images, enabling various applications such as image classification and object detection. The MATLAB implementation typically involves using the Deep Learning Toolbox, where key functions like convolution2dLayer create convolutional layers for feature detection, maxPooling2dLayer performs downsampling to reduce spatial dimensions, and fullyConnectedLayer combines features for final classification. The training process employs backpropagation algorithms with optimization methods like stochastic gradient descent to minimize classification errors. We believe this research contributes to the advancement of image processing and computer vision fields by providing practical implementation insights.