Image recognition is the process of identifying and detecting an object or a feature in a digital image or video. This concept is used in many applications like systems for factory automation, toll booth monitoring, and security surveillance. Typical image recognition algorithms include:
- Optical character recognition
- Pattern matching and gradient matching
- Face recognition
- License plate matching
- Scene identification or scene change detection
Machine learning and deep learning methods can be a useful approach to image recognition.
Image Recognition Using Machine Learning
A machine learning approach to image recognition involves identifying and extracting key features from images and using them as input to a machine learning model.
An example of this is classifying digits using HOG features and an SVM classifier.
Digit classification using histogram of oriented gradients (HOG) feature extraction of image (top) and SVMs (bottom). See example for details and source code.
Image Recognition Using Deep Learning
A deep learning approach to image recognition may involve the use of a convolutional neural network to automatically learn relevant features from sample images and automatically identify those features in new images.
An effective approach for image recognition includes using a technical computing environment for data analysis, visualization, and algorithm development.
For more information, see Computer Vision Toolbox™, Statistics and Machine Learning Toolbox™, and Deep Learning Toolbox™.
