Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification.
Pattern recognition has applications in computer vision, radar processing, speech recognition, and text classification.
Supervised Classification
The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label.
In computer vision, supervised pattern recognition techniques are used for optical character recognition (OCR), face detection, object detection, and object classification.
Detecting people using support vector machines (SVM) and HOG feature extraction. See documentation for details.
Unsupervised Classification
The unsupervised classification method works by finding hidden structures in unlabeled data using segmentation or clustering techniques. Common unsupervised classification methods include:
- K-means clustering
- Gaussian mixture models
- Hidden Markov models
In image processing and computer vision, unsupervised pattern recognition techniques are used for object detection and image segmentation.
Detecting moving objects by classifying image pixels in into foreground (white pixels) and background (black pixels) using Gaussian mixture models. See example for details.
Color-based image segmentation using k-means clustering.
For details, see Computer Vision Toolbox™, Image Processing Toolbox™, and Statistics and Machine Learning Toolbox™, which are used with MATLAB®.
