Image recognition is a subset of computer vision, a field that focuses on training and developing computers to gain a high-level understanding from digital images or videos. In the context of machine learning or deep learning, image recognition is a process which identifies and detects an object or attribute in a digital image or video, it can be a particular feature or the full image itself.
How Image Recognition works
Image recognition algorithms take an image input (or a frame of a video), treat it as a set of pixels, and analyze these pixels individually by assigning each one a value. These pixel values are then processed using complex mathematical functions running on powerful computer processors to interpret the image.
There are several methods for image recognition in machine learning, worth to mention convolutional neural networks (CNNs), which are popular for their accuracy and efficiency. CNNs are a class of deep learning models that are primarily used to analyze visual data.
Training phase is the key part in image recognition. During this phase, the algorithm goes through a multitude of images, learning to recognize various objects, animals, landmarks or whatever else it needs to recognize, by comparing the pixel values of the image to the corresponding output, making adjustments to weights and biases in the network.
Once the machine learning model is trained, it can be exposed to new images, where it classifies the objects in the image by reducing the differences between the new input and what it has learned so far.
Applications of image recognition include autonomous cars, facial recognition, medical imaging diagnostics, and more.
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