In the process of neural network image recognition, the vector or raster encoding of the image is turned into constructs that depict physical objects and features. Computer vision systems can logically analyze these constructs, first by simplifying images and extracting the most important information, then by organizing data through feature extraction and classification.
Finally, computer vision systems use classification or other algorithms to make a decision about the image or part of it – which category they belong to, or how they can best be described.
Image Recognition Algorithms
One type of image recognition algorithm is an image classifier. It takes an image (or part of an image) as an input and predicts what the image contains. The output is a class label, such as dog, cat or table. The algorithm needs to be trained to learn and distinguish between classes.
In a simple case, to create a classification algorithm that can identify images with dogs, you’ll train a neural network with thousands of images of dogs, and thousands of images of backgrounds without dogs. The algorithm will learn to extract the features that identify a “dog” object and correctly classify images that contain dogs. While most image recognition algorithms are classifiers, other algorithms can be used to perform more complex activities. For example, a Recurrent Neural Network can be used to automatically write captions describing the content of an image.
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