Image Recognition

Recognition methods in image processing

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:

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.

Explore deep learning fundamentals in this MATLAB Tech Talk. You’ll learn why deep learning has become so popular, and you’ll walk through 3 concepts: what deep learning is, how it is used in the real world, and how you can get started.
Learn about the differences between deep learning and machine learning in this MATLAB Tech Talk. Walk through several examples, and learn about how decide which method to use.

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 System Toolbox™, Statistics and Machine Learning Toolbox™, and Neural Network Toolbox™.

See also: image reconstruction, image transform, image enhancement, image segmentation, image processing and computer vision, MATLAB and OpenCV, face recognition, object detection, object recognition, feature extraction, stereo vision, optical flow, RANSAC, pattern recognition, deep learning