Feature extraction a type of dimensionality reduction that efficiently represents interesting parts of an image as a compact feature vector. This approach is useful when image sizes are large and a reduced feature representation is required to quickly complete tasks such as image matching and retrieval.
Feature detection, feature extraction, and matching are often combined to solve common computer vision problems such as object detection and recognition, content-based image retrieval, face detection and recognition, and texture classification.
Common feature extraction techniques include Histogram of Oriented Gradients (HOG), Speeded Up Robust Features (SURF), Local Binary Patterns (LBP), Haar wavelets, and color histograms.
See also: MATLAB, Computer Vision System Toolbox, feature detection, feature matching, object detection, image stabilization, image processing and computer vision, face recognition, image recognition, object detection, object recognition, digital image processing, Optical Flow, ransac, pattern recognition, point cloud, deep learning
In this course you’ll determine how to use unsupervised learning techniques to discover features in large data sets and supervised learning techniques to build predictive models.