Feature extraction on images works by identifying significant patterns or characteristics that represent the image's content. Traditional methods involve detecting edges, textures, or shapes using algorithms like SIFT, SURF, or HOG.
In deep learning, convolutional neural networks (CNNs) automate feature extraction by learning hierarchical patterns from raw data during training. Initial layers detect basic features like edges, while deeper layers capture complex patterns like objects or scenes.
Extracted features are then used for tasks like classification, clustering, or object detection, making feature extraction a crucial step in computer vision workflows.